mathlea_10 <- get_data("EDFacts_math_achievement_lea_2010_2019")
rlalea_10 <- get_data("EDFacts_rla_achievement_lea_2010_2019")
fiscal2010 <- get_data("NCES_CCD_fiscal_district_2010")
Research Question:
How do high school students’ subgroup makeup (i.e., Race/ethnicity, Male vs. Female, economically disadvantaged, Limited English, Migrant status, Disability status, and Homelessness) differ among states/regions?
Datasets:
skim(mathlea_10)
| Name | mathlea_10 |
| Number of rows | 15747 |
| Number of columns | 232 |
| _______________________ | |
| Column type frequency: | |
| character | 118 |
| numeric | 113 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ALL_MTH00PCTPROF | 0 | 1.00 | 1 | 5 | 0 | 134 | 0 |
| ALL_MTH03PCTPROF | 1367 | 0.91 | 2 | 5 | 0 | 114 | 0 |
| ALL_MTH04PCTPROF | 1368 | 0.91 | 2 | 5 | 0 | 122 | 0 |
| ALL_MTH05PCTPROF | 1356 | 0.91 | 2 | 5 | 0 | 122 | 0 |
| ALL_MTH06PCTPROF | 1354 | 0.91 | 1 | 5 | 0 | 123 | 0 |
| ALL_MTH07PCTPROF | 1779 | 0.89 | 1 | 5 | 0 | 125 | 0 |
| ALL_MTH08PCTPROF | 1836 | 0.88 | 1 | 5 | 0 | 129 | 0 |
| ALL_MTHHSPCTPROF | 3666 | 0.77 | 1 | 5 | 0 | 131 | 0 |
| CWD_MTH00PCTPROF | 189 | 0.99 | 1 | 5 | 0 | 127 | 0 |
| CWD_MTH03PCTPROF | 2014 | 0.87 | 2 | 5 | 0 | 105 | 0 |
| CWD_MTH04PCTPROF | 2002 | 0.87 | 1 | 5 | 0 | 111 | 0 |
| CWD_MTH05PCTPROF | 2006 | 0.87 | 1 | 5 | 0 | 116 | 0 |
| CWD_MTH06PCTPROF | 2031 | 0.87 | 1 | 5 | 0 | 108 | 0 |
| CWD_MTH07PCTPROF | 2384 | 0.85 | 1 | 5 | 0 | 108 | 0 |
| CWD_MTH08PCTPROF | 2463 | 0.84 | 1 | 5 | 0 | 114 | 0 |
| CWD_MTHHSPCTPROF | 4155 | 0.74 | 1 | 5 | 0 | 108 | 0 |
| ECD_MTH00PCTPROF | 405 | 0.97 | 1 | 5 | 0 | 129 | 0 |
| ECD_MTH03PCTPROF | 1792 | 0.89 | 2 | 5 | 0 | 116 | 0 |
| ECD_MTH04PCTPROF | 1776 | 0.89 | 2 | 5 | 0 | 120 | 0 |
| ECD_MTH05PCTPROF | 1754 | 0.89 | 1 | 5 | 0 | 123 | 0 |
| ECD_MTH06PCTPROF | 1772 | 0.89 | 1 | 5 | 0 | 123 | 0 |
| ECD_MTH07PCTPROF | 2169 | 0.86 | 1 | 5 | 0 | 124 | 0 |
| ECD_MTH08PCTPROF | 2215 | 0.86 | 1 | 5 | 0 | 128 | 0 |
| ECD_MTHHSPCTPROF | 3952 | 0.75 | 1 | 5 | 0 | 130 | 0 |
| FIPST | 0 | 1.00 | 2 | 2 | 0 | 51 | 0 |
| F_MTH00PCTPROF | 47 | 1.00 | 1 | 5 | 0 | 125 | 0 |
| F_MTH03PCTPROF | 1459 | 0.91 | 2 | 5 | 0 | 105 | 0 |
| F_MTH04PCTPROF | 1467 | 0.91 | 2 | 5 | 0 | 114 | 0 |
| F_MTH05PCTPROF | 1463 | 0.91 | 2 | 5 | 0 | 115 | 0 |
| F_MTH06PCTPROF | 1460 | 0.91 | 1 | 5 | 0 | 117 | 0 |
| F_MTH07PCTPROF | 1877 | 0.88 | 1 | 5 | 0 | 119 | 0 |
| F_MTH08PCTPROF | 1933 | 0.88 | 1 | 5 | 0 | 127 | 0 |
| F_MTHHSPCTPROF | 3743 | 0.76 | 1 | 5 | 0 | 125 | 0 |
| FILEURL | 0 | 1.00 | 88 | 88 | 0 | 1 | 0 |
| HOM_MTH00PCTPROF | 9382 | 0.40 | 2 | 5 | 0 | 95 | 0 |
| HOM_MTH03PCTPROF | 10803 | 0.31 | 2 | 5 | 0 | 40 | 0 |
| HOM_MTH04PCTPROF | 10855 | 0.31 | 2 | 5 | 0 | 39 | 0 |
| HOM_MTH05PCTPROF | 10855 | 0.31 | 2 | 5 | 0 | 41 | 0 |
| HOM_MTH06PCTPROF | 10923 | 0.31 | 1 | 5 | 0 | 40 | 0 |
| HOM_MTH07PCTPROF | 11042 | 0.30 | 1 | 5 | 0 | 39 | 0 |
| HOM_MTH08PCTPROF | 11069 | 0.30 | 1 | 5 | 0 | 40 | 0 |
| HOM_MTHHSPCTPROF | 11704 | 0.26 | 1 | 5 | 0 | 46 | 0 |
| LEAID | 0 | 1.00 | 7 | 7 | 0 | 15747 | 0 |
| LEANM09 | 0 | 1.00 | 3 | 60 | 0 | 15465 | 0 |
| LEP_MTH00PCTPROF | 4294 | 0.73 | 1 | 5 | 0 | 125 | 0 |
| LEP_MTH03PCTPROF | 6354 | 0.60 | 2 | 5 | 0 | 110 | 0 |
| LEP_MTH04PCTPROF | 6505 | 0.59 | 1 | 5 | 0 | 112 | 0 |
| LEP_MTH05PCTPROF | 6738 | 0.57 | 1 | 5 | 0 | 116 | 0 |
| LEP_MTH06PCTPROF | 6940 | 0.56 | 1 | 5 | 0 | 103 | 0 |
| LEP_MTH07PCTPROF | 7267 | 0.54 | 1 | 5 | 0 | 104 | 0 |
| LEP_MTH08PCTPROF | 7406 | 0.53 | 1 | 5 | 0 | 102 | 0 |
| LEP_MTHHSPCTPROF | 8633 | 0.45 | 1 | 5 | 0 | 85 | 0 |
| MAM_MTH00PCTPROF | 4167 | 0.74 | 2 | 5 | 0 | 99 | 0 |
| MAM_MTH03PCTPROF | 8056 | 0.49 | 2 | 5 | 0 | 43 | 0 |
| MAM_MTH04PCTPROF | 8050 | 0.49 | 2 | 5 | 0 | 45 | 0 |
| MAM_MTH05PCTPROF | 8049 | 0.49 | 2 | 5 | 0 | 43 | 0 |
| MAM_MTH06PCTPROF | 7977 | 0.49 | 2 | 5 | 0 | 41 | 0 |
| MAM_MTH07PCTPROF | 8141 | 0.48 | 2 | 5 | 0 | 41 | 0 |
| MAM_MTH08PCTPROF | 8129 | 0.48 | 2 | 5 | 0 | 40 | 0 |
| MAM_MTHHSPCTPROF | 8926 | 0.43 | 2 | 5 | 0 | 43 | 0 |
| MAS_MTH00PCTPROF | 3309 | 0.79 | 2 | 5 | 0 | 92 | 0 |
| MAS_MTH03PCTPROF | 6732 | 0.57 | 2 | 5 | 0 | 61 | 0 |
| MAS_MTH04PCTPROF | 6711 | 0.57 | 2 | 5 | 0 | 63 | 0 |
| MAS_MTH05PCTPROF | 6747 | 0.57 | 2 | 5 | 0 | 68 | 0 |
| MAS_MTH06PCTPROF | 6816 | 0.57 | 2 | 5 | 0 | 70 | 0 |
| MAS_MTH07PCTPROF | 7023 | 0.55 | 2 | 5 | 0 | 75 | 0 |
| MAS_MTH08PCTPROF | 7065 | 0.55 | 2 | 5 | 0 | 80 | 0 |
| MAS_MTHHSPCTPROF | 7983 | 0.49 | 2 | 5 | 0 | 81 | 0 |
| MBL_MTH00PCTPROF | 1951 | 0.88 | 1 | 5 | 0 | 126 | 0 |
| MBL_MTH03PCTPROF | 5389 | 0.66 | 2 | 5 | 0 | 109 | 0 |
| MBL_MTH04PCTPROF | 5307 | 0.66 | 2 | 5 | 0 | 108 | 0 |
| MBL_MTH05PCTPROF | 5249 | 0.67 | 2 | 5 | 0 | 118 | 0 |
| MBL_MTH06PCTPROF | 5239 | 0.67 | 2 | 5 | 0 | 114 | 0 |
| MBL_MTH07PCTPROF | 5579 | 0.65 | 1 | 5 | 0 | 115 | 0 |
| MBL_MTH08PCTPROF | 5618 | 0.64 | 1 | 5 | 0 | 122 | 0 |
| MBL_MTHHSPCTPROF | 6772 | 0.57 | 1 | 5 | 0 | 125 | 0 |
| MHI_MTH00PCTPROF | 1348 | 0.91 | 2 | 5 | 0 | 121 | 0 |
| MHI_MTH03PCTPROF | 4108 | 0.74 | 2 | 5 | 0 | 103 | 0 |
| MHI_MTH04PCTPROF | 4194 | 0.73 | 2 | 5 | 0 | 110 | 0 |
| MHI_MTH05PCTPROF | 4179 | 0.73 | 2 | 5 | 0 | 111 | 0 |
| MHI_MTH06PCTPROF | 4231 | 0.73 | 1 | 5 | 0 | 116 | 0 |
| MHI_MTH07PCTPROF | 4505 | 0.71 | 1 | 5 | 0 | 115 | 0 |
| MHI_MTH08PCTPROF | 4630 | 0.71 | 1 | 5 | 0 | 127 | 0 |
| MHI_MTHHSPCTPROF | 6008 | 0.62 | 1 | 5 | 0 | 118 | 0 |
| MIG_MTH00PCTPROF | 10326 | 0.34 | 2 | 5 | 0 | 85 | 0 |
| MIG_MTH03PCTPROF | 11597 | 0.26 | 2 | 5 | 0 | 35 | 0 |
| MIG_MTH04PCTPROF | 11613 | 0.26 | 2 | 5 | 0 | 36 | 0 |
| MIG_MTH05PCTPROF | 11628 | 0.26 | 2 | 5 | 0 | 36 | 0 |
| MIG_MTH06PCTPROF | 11676 | 0.26 | 2 | 5 | 0 | 36 | 0 |
| MIG_MTH07PCTPROF | 11814 | 0.25 | 2 | 5 | 0 | 30 | 0 |
| MIG_MTH08PCTPROF | 11817 | 0.25 | 2 | 5 | 0 | 37 | 0 |
| MIG_MTHHSPCTPROF | 12362 | 0.21 | 2 | 5 | 0 | 38 | 0 |
| MTR_MTH00PCTPROF | 9021 | 0.43 | 2 | 5 | 0 | 92 | 0 |
| MTR_MTH03PCTPROF | 10440 | 0.34 | 2 | 5 | 0 | 40 | 0 |
| MTR_MTH04PCTPROF | 10527 | 0.33 | 2 | 5 | 0 | 42 | 0 |
| MTR_MTH05PCTPROF | 10507 | 0.33 | 2 | 5 | 0 | 41 | 0 |
| MTR_MTH06PCTPROF | 10600 | 0.33 | 2 | 5 | 0 | 39 | 0 |
| MTR_MTH07PCTPROF | 10776 | 0.32 | 2 | 5 | 0 | 44 | 0 |
| MTR_MTH08PCTPROF | 10834 | 0.31 | 2 | 5 | 0 | 45 | 0 |
| MTR_MTHHSPCTPROF | 11647 | 0.26 | 2 | 5 | 0 | 47 | 0 |
| MWH_MTH00PCTPROF | 430 | 0.97 | 2 | 5 | 0 | 122 | 0 |
| MWH_MTH03PCTPROF | 1889 | 0.88 | 2 | 5 | 0 | 101 | 0 |
| MWH_MTH04PCTPROF | 1889 | 0.88 | 2 | 5 | 0 | 111 | 0 |
| MWH_MTH05PCTPROF | 1912 | 0.88 | 2 | 5 | 0 | 115 | 0 |
| MWH_MTH06PCTPROF | 1877 | 0.88 | 2 | 5 | 0 | 113 | 0 |
| MWH_MTH07PCTPROF | 2251 | 0.86 | 2 | 5 | 0 | 116 | 0 |
| MWH_MTH08PCTPROF | 2281 | 0.86 | 2 | 5 | 0 | 120 | 0 |
| MWH_MTHHSPCTPROF | 3955 | 0.75 | 2 | 5 | 0 | 119 | 0 |
| M_MTH00PCTPROF | 28 | 1.00 | 1 | 5 | 0 | 124 | 0 |
| M_MTH03PCTPROF | 1428 | 0.91 | 2 | 5 | 0 | 106 | 0 |
| M_MTH04PCTPROF | 1418 | 0.91 | 2 | 5 | 0 | 115 | 0 |
| M_MTH05PCTPROF | 1412 | 0.91 | 2 | 5 | 0 | 115 | 0 |
| M_MTH06PCTPROF | 1410 | 0.91 | 1 | 5 | 0 | 117 | 0 |
| M_MTH07PCTPROF | 1830 | 0.88 | 1 | 5 | 0 | 125 | 0 |
| M_MTH08PCTPROF | 1891 | 0.88 | 1 | 5 | 0 | 126 | 0 |
| M_MTHHSPCTPROF | 3712 | 0.76 | 1 | 5 | 0 | 122 | 0 |
| STNAM | 0 | 1.00 | 4 | 24 | 0 | 51 | 0 |
| PIPELINE | 0 | 1.00 | 38 | 38 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ALL_MTH00NUMVALID | 0 | 1.00 | 1627.91 | 6374.70 | 0 | 149 | 447.0 | 1284.00 | 355121 | ▇▁▁▁▁ |
| ALL_MTH03NUMVALID | 1367 | 0.91 | 255.01 | 961.48 | 0 | 28 | 74.0 | 201.00 | 51775 | ▇▁▁▁▁ |
| ALL_MTH04NUMVALID | 1368 | 0.91 | 254.95 | 951.13 | 0 | 28 | 75.0 | 202.00 | 51736 | ▇▁▁▁▁ |
| ALL_MTH05NUMVALID | 1356 | 0.91 | 251.32 | 935.44 | 0 | 28 | 75.0 | 202.00 | 51537 | ▇▁▁▁▁ |
| ALL_MTH06NUMVALID | 1354 | 0.91 | 249.75 | 918.15 | 0 | 28 | 76.0 | 204.00 | 49264 | ▇▁▁▁▁ |
| ALL_MTH07NUMVALID | 1779 | 0.89 | 254.91 | 934.73 | 0 | 30 | 80.0 | 211.00 | 49880 | ▇▁▁▁▁ |
| ALL_MTH08NUMVALID | 1836 | 0.88 | 258.21 | 929.41 | 0 | 30 | 81.0 | 214.00 | 50322 | ▇▁▁▁▁ |
| ALL_MTHHSNUMVALID | 3666 | 0.77 | 325.95 | 1284.57 | 0 | 39 | 99.0 | 248.00 | 50607 | ▇▁▁▁▁ |
| CWD_MTH00NUMVALID | 189 | 0.99 | 214.49 | 887.63 | 0 | 21 | 60.0 | 175.00 | 58764 | ▇▁▁▁▁ |
| CWD_MTH03NUMVALID | 2014 | 0.87 | 35.38 | 138.93 | 0 | 4 | 11.0 | 29.00 | 9315 | ▇▁▁▁▁ |
| CWD_MTH04NUMVALID | 2002 | 0.87 | 36.65 | 144.13 | 0 | 4 | 11.0 | 30.00 | 9704 | ▇▁▁▁▁ |
| CWD_MTH05NUMVALID | 2006 | 0.87 | 36.25 | 143.00 | 0 | 4 | 11.0 | 30.00 | 9316 | ▇▁▁▁▁ |
| CWD_MTH06NUMVALID | 2031 | 0.87 | 35.06 | 136.40 | 0 | 4 | 11.0 | 29.00 | 8970 | ▇▁▁▁▁ |
| CWD_MTH07NUMVALID | 2384 | 0.85 | 34.82 | 139.01 | 0 | 4 | 11.0 | 29.00 | 9421 | ▇▁▁▁▁ |
| CWD_MTH08NUMVALID | 2463 | 0.84 | 34.77 | 129.70 | 0 | 4 | 12.0 | 30.00 | 7754 | ▇▁▁▁▁ |
| CWD_MTHHSNUMVALID | 4155 | 0.74 | 38.07 | 144.22 | 0 | 5 | 13.0 | 31.00 | 5312 | ▇▁▁▁▁ |
| ECD_MTH00NUMVALID | 405 | 0.97 | 834.25 | 4424.40 | 0 | 64 | 182.0 | 517.00 | 293101 | ▇▁▁▁▁ |
| ECD_MTH03NUMVALID | 1792 | 0.89 | 142.01 | 707.64 | 0 | 12 | 33.0 | 88.00 | 43322 | ▇▁▁▁▁ |
| ECD_MTH04NUMVALID | 1776 | 0.89 | 139.39 | 691.80 | 0 | 12 | 33.0 | 87.50 | 43224 | ▇▁▁▁▁ |
| ECD_MTH05NUMVALID | 1754 | 0.89 | 135.33 | 677.18 | 0 | 12 | 32.0 | 86.00 | 43027 | ▇▁▁▁▁ |
| ECD_MTH06NUMVALID | 1772 | 0.89 | 131.76 | 657.63 | 0 | 12 | 32.0 | 86.00 | 41090 | ▇▁▁▁▁ |
| ECD_MTH07NUMVALID | 2169 | 0.86 | 130.72 | 663.07 | 0 | 13 | 32.0 | 85.00 | 41130 | ▇▁▁▁▁ |
| ECD_MTH08NUMVALID | 2215 | 0.86 | 128.30 | 645.08 | 0 | 12 | 31.0 | 83.00 | 41034 | ▇▁▁▁▁ |
| ECD_MTHHSNUMVALID | 3952 | 0.75 | 137.66 | 717.20 | 0 | 14 | 33.0 | 85.50 | 40274 | ▇▁▁▁▁ |
| F_MTH00NUMVALID | 47 | 1.00 | 797.18 | 3127.18 | 0 | 73 | 219.0 | 625.25 | 174078 | ▇▁▁▁▁ |
| F_MTH03NUMVALID | 1459 | 0.91 | 124.90 | 469.39 | 0 | 14 | 36.0 | 98.00 | 25434 | ▇▁▁▁▁ |
| F_MTH04NUMVALID | 1467 | 0.91 | 125.04 | 465.82 | 0 | 14 | 37.0 | 99.00 | 25386 | ▇▁▁▁▁ |
| F_MTH05NUMVALID | 1463 | 0.91 | 123.30 | 458.45 | 0 | 14 | 37.0 | 100.00 | 25212 | ▇▁▁▁▁ |
| F_MTH06NUMVALID | 1460 | 0.91 | 122.60 | 449.49 | 0 | 14 | 37.0 | 100.00 | 23946 | ▇▁▁▁▁ |
| F_MTH07NUMVALID | 1877 | 0.88 | 125.05 | 457.22 | 0 | 15 | 39.0 | 103.00 | 24511 | ▇▁▁▁▁ |
| F_MTH08NUMVALID | 1933 | 0.88 | 126.89 | 456.86 | 0 | 15 | 40.0 | 105.00 | 24535 | ▇▁▁▁▁ |
| F_MTHHSNUMVALID | 3743 | 0.76 | 162.07 | 640.96 | 0 | 19 | 49.0 | 124.00 | 25054 | ▇▁▁▁▁ |
| HOM_MTH00NUMVALID | 9382 | 0.40 | 36.95 | 173.69 | 0 | 0 | 3.0 | 15.00 | 5981 | ▇▁▁▁▁ |
| HOM_MTH03NUMVALID | 10803 | 0.31 | 8.11 | 32.35 | 0 | 0 | 1.0 | 4.00 | 995 | ▇▁▁▁▁ |
| HOM_MTH04NUMVALID | 10855 | 0.31 | 7.72 | 30.59 | 0 | 0 | 1.0 | 4.00 | 817 | ▇▁▁▁▁ |
| HOM_MTH05NUMVALID | 10855 | 0.31 | 7.29 | 29.57 | 0 | 0 | 1.0 | 4.00 | 876 | ▇▁▁▁▁ |
| HOM_MTH06NUMVALID | 10923 | 0.31 | 6.90 | 28.44 | 0 | 0 | 1.0 | 4.00 | 859 | ▇▁▁▁▁ |
| HOM_MTH07NUMVALID | 11042 | 0.30 | 6.58 | 28.52 | 0 | 0 | 1.0 | 4.00 | 907 | ▇▁▁▁▁ |
| HOM_MTH08NUMVALID | 11069 | 0.30 | 6.20 | 27.95 | 0 | 0 | 1.0 | 3.00 | 968 | ▇▁▁▁▁ |
| HOM_MTHHSNUMVALID | 11704 | 0.26 | 7.04 | 32.67 | 0 | 0 | 1.0 | 4.00 | 1086 | ▇▁▁▁▁ |
| LEP_MTH00NUMVALID | 4294 | 0.73 | 188.97 | 1270.02 | 0 | 2 | 10.0 | 56.00 | 95371 | ▇▁▁▁▁ |
| LEP_MTH03NUMVALID | 6354 | 0.60 | 52.74 | 316.59 | 0 | 1 | 4.0 | 18.00 | 20260 | ▇▁▁▁▁ |
| LEP_MTH04NUMVALID | 6505 | 0.59 | 44.48 | 268.76 | 0 | 1 | 3.0 | 15.00 | 17230 | ▇▁▁▁▁ |
| LEP_MTH05NUMVALID | 6738 | 0.57 | 36.13 | 212.71 | 0 | 1 | 3.0 | 13.00 | 14017 | ▇▁▁▁▁ |
| LEP_MTH06NUMVALID | 6940 | 0.56 | 30.29 | 169.92 | 0 | 1 | 3.0 | 12.00 | 10875 | ▇▁▁▁▁ |
| LEP_MTH07NUMVALID | 7267 | 0.54 | 28.70 | 167.41 | 0 | 1 | 2.0 | 11.00 | 11046 | ▇▁▁▁▁ |
| LEP_MTH08NUMVALID | 7406 | 0.53 | 26.58 | 159.77 | 0 | 1 | 2.0 | 10.00 | 10902 | ▇▁▁▁▁ |
| LEP_MTHHSNUMVALID | 8633 | 0.45 | 28.18 | 189.85 | 0 | 0 | 2.0 | 10.00 | 11041 | ▇▁▁▁▁ |
| MAM_MTH00NUMVALID | 4167 | 0.74 | 27.42 | 118.62 | 0 | 1 | 3.0 | 13.00 | 5454 | ▇▁▁▁▁ |
| MAM_MTH03NUMVALID | 8056 | 0.49 | 5.96 | 21.55 | 0 | 0 | 1.0 | 4.00 | 846 | ▇▁▁▁▁ |
| MAM_MTH04NUMVALID | 8050 | 0.49 | 5.96 | 22.13 | 0 | 0 | 1.0 | 4.00 | 930 | ▇▁▁▁▁ |
| MAM_MTH05NUMVALID | 8049 | 0.49 | 5.94 | 21.47 | 0 | 0 | 1.0 | 4.00 | 842 | ▇▁▁▁▁ |
| MAM_MTH06NUMVALID | 7977 | 0.49 | 5.83 | 20.34 | 0 | 0 | 1.0 | 4.00 | 796 | ▇▁▁▁▁ |
| MAM_MTH07NUMVALID | 8141 | 0.48 | 5.85 | 20.12 | 0 | 0 | 1.0 | 4.00 | 749 | ▇▁▁▁▁ |
| MAM_MTH08NUMVALID | 8129 | 0.48 | 5.74 | 19.84 | 0 | 0 | 1.0 | 4.00 | 738 | ▇▁▁▁▁ |
| MAM_MTHHSNUMVALID | 8926 | 0.43 | 6.81 | 23.86 | 0 | 0 | 1.0 | 4.00 | 782 | ▇▁▁▁▁ |
| MAS_MTH00NUMVALID | 3309 | 0.79 | 104.60 | 868.65 | 0 | 2 | 6.0 | 28.00 | 74447 | ▇▁▁▁▁ |
| MAS_MTH03NUMVALID | 6732 | 0.57 | 20.82 | 144.82 | 0 | 1 | 2.0 | 8.00 | 10783 | ▇▁▁▁▁ |
| MAS_MTH04NUMVALID | 6711 | 0.57 | 21.07 | 148.93 | 0 | 1 | 2.0 | 8.00 | 11147 | ▇▁▁▁▁ |
| MAS_MTH05NUMVALID | 6747 | 0.57 | 19.93 | 142.78 | 0 | 1 | 2.0 | 7.00 | 10822 | ▇▁▁▁▁ |
| MAS_MTH06NUMVALID | 6816 | 0.57 | 20.09 | 142.97 | 0 | 1 | 2.0 | 8.00 | 10584 | ▇▁▁▁▁ |
| MAS_MTH07NUMVALID | 7023 | 0.55 | 20.70 | 143.74 | 0 | 1 | 2.0 | 8.00 | 10239 | ▇▁▁▁▁ |
| MAS_MTH08NUMVALID | 7065 | 0.55 | 20.97 | 145.91 | 0 | 1 | 2.0 | 8.00 | 10299 | ▇▁▁▁▁ |
| MAS_MTHHSNUMVALID | 7983 | 0.49 | 25.96 | 189.64 | 0 | 1 | 2.0 | 8.00 | 10573 | ▇▁▁▁▁ |
| MBL_MTH00NUMVALID | 1951 | 0.88 | 296.34 | 1846.23 | 0 | 3 | 13.0 | 91.25 | 87843 | ▇▁▁▁▁ |
| MBL_MTH03NUMVALID | 5389 | 0.66 | 56.56 | 310.58 | 0 | 1 | 4.0 | 24.00 | 13822 | ▇▁▁▁▁ |
| MBL_MTH04NUMVALID | 5307 | 0.66 | 56.32 | 300.46 | 0 | 1 | 4.0 | 24.00 | 12539 | ▇▁▁▁▁ |
| MBL_MTH05NUMVALID | 5249 | 0.67 | 54.86 | 295.57 | 0 | 1 | 4.0 | 23.00 | 12612 | ▇▁▁▁▁ |
| MBL_MTH06NUMVALID | 5239 | 0.67 | 54.78 | 293.46 | 0 | 1 | 4.0 | 23.25 | 13169 | ▇▁▁▁▁ |
| MBL_MTH07NUMVALID | 5579 | 0.65 | 55.27 | 294.07 | 0 | 1 | 4.0 | 25.00 | 12780 | ▇▁▁▁▁ |
| MBL_MTH08NUMVALID | 5618 | 0.64 | 56.25 | 296.62 | 0 | 1 | 4.0 | 24.00 | 12982 | ▇▁▁▁▁ |
| MBL_MTHHSNUMVALID | 6772 | 0.57 | 70.33 | 390.92 | 0 | 1 | 4.0 | 28.00 | 14271 | ▇▁▁▁▁ |
| MHI_MTH00NUMVALID | 1348 | 0.91 | 407.63 | 3799.12 | 0 | 5 | 21.0 | 103.00 | 262592 | ▇▁▁▁▁ |
| MHI_MTH03NUMVALID | 4108 | 0.74 | 76.74 | 618.54 | 0 | 1 | 5.0 | 24.00 | 38320 | ▇▁▁▁▁ |
| MHI_MTH04NUMVALID | 4194 | 0.73 | 75.49 | 621.21 | 0 | 1 | 5.0 | 23.00 | 38689 | ▇▁▁▁▁ |
| MHI_MTH05NUMVALID | 4179 | 0.73 | 73.55 | 611.80 | 0 | 1 | 5.0 | 23.00 | 38328 | ▇▁▁▁▁ |
| MHI_MTH06NUMVALID | 4231 | 0.73 | 72.24 | 602.96 | 0 | 1 | 5.0 | 23.00 | 38729 | ▇▁▁▁▁ |
| MHI_MTH07NUMVALID | 4505 | 0.71 | 72.75 | 620.83 | 0 | 1 | 5.0 | 22.00 | 40539 | ▇▁▁▁▁ |
| MHI_MTH08NUMVALID | 4630 | 0.71 | 72.23 | 606.06 | 0 | 1 | 5.0 | 22.00 | 37538 | ▇▁▁▁▁ |
| MHI_MTHHSNUMVALID | 6008 | 0.62 | 82.21 | 678.56 | 0 | 1 | 5.0 | 24.00 | 37326 | ▇▁▁▁▁ |
| MIG_MTH00NUMVALID | 10326 | 0.34 | 24.42 | 105.06 | 0 | 0 | 1.0 | 8.00 | 2574 | ▇▁▁▁▁ |
| MIG_MTH03NUMVALID | 11597 | 0.26 | 4.86 | 18.70 | 0 | 0 | 0.0 | 2.00 | 400 | ▇▁▁▁▁ |
| MIG_MTH04NUMVALID | 11613 | 0.26 | 4.71 | 17.98 | 0 | 0 | 0.0 | 2.00 | 347 | ▇▁▁▁▁ |
| MIG_MTH05NUMVALID | 11628 | 0.26 | 4.67 | 17.89 | 0 | 0 | 0.0 | 2.00 | 356 | ▇▁▁▁▁ |
| MIG_MTH06NUMVALID | 11676 | 0.26 | 4.59 | 17.43 | 0 | 0 | 0.0 | 2.00 | 357 | ▇▁▁▁▁ |
| MIG_MTH07NUMVALID | 11814 | 0.25 | 4.68 | 18.08 | 0 | 0 | 0.0 | 2.00 | 391 | ▇▁▁▁▁ |
| MIG_MTH08NUMVALID | 11817 | 0.25 | 4.59 | 17.71 | 0 | 0 | 0.0 | 2.00 | 381 | ▇▁▁▁▁ |
| MIG_MTHHSNUMVALID | 12362 | 0.21 | 5.43 | 22.77 | 0 | 0 | 0.0 | 2.00 | 379 | ▇▁▁▁▁ |
| MTR_MTH00NUMVALID | 9021 | 0.43 | 45.37 | 173.09 | 0 | 2 | 8.0 | 28.00 | 6292 | ▇▁▁▁▁ |
| MTR_MTH03NUMVALID | 10440 | 0.34 | 9.70 | 32.82 | 0 | 0 | 2.0 | 7.00 | 805 | ▇▁▁▁▁ |
| MTR_MTH04NUMVALID | 10527 | 0.33 | 9.11 | 30.23 | 0 | 0 | 2.0 | 7.00 | 880 | ▇▁▁▁▁ |
| MTR_MTH05NUMVALID | 10507 | 0.33 | 8.59 | 29.93 | 0 | 0 | 2.0 | 6.00 | 1044 | ▇▁▁▁▁ |
| MTR_MTH06NUMVALID | 10600 | 0.33 | 8.31 | 28.75 | 0 | 0 | 2.0 | 6.00 | 1031 | ▇▁▁▁▁ |
| MTR_MTH07NUMVALID | 10776 | 0.32 | 8.12 | 27.96 | 0 | 0 | 2.0 | 6.00 | 996 | ▇▁▁▁▁ |
| MTR_MTH08NUMVALID | 10834 | 0.31 | 7.86 | 27.34 | 0 | 0 | 2.0 | 6.00 | 975 | ▇▁▁▁▁ |
| MTR_MTHHSNUMVALID | 11647 | 0.26 | 9.60 | 47.00 | 0 | 0 | 1.5 | 6.00 | 1894 | ▇▁▁▁▁ |
| MWH_MTH00NUMVALID | 430 | 0.97 | 890.47 | 2126.33 | 0 | 88 | 321.0 | 907.00 | 56161 | ▇▁▁▁▁ |
| MWH_MTH03NUMVALID | 1889 | 0.88 | 136.04 | 303.38 | 0 | 17 | 54.0 | 140.00 | 8094 | ▇▁▁▁▁ |
| MWH_MTH04NUMVALID | 1889 | 0.88 | 137.50 | 305.12 | 0 | 18 | 55.0 | 142.00 | 8120 | ▇▁▁▁▁ |
| MWH_MTH05NUMVALID | 1912 | 0.88 | 137.61 | 301.39 | 0 | 18 | 55.0 | 143.00 | 8171 | ▇▁▁▁▁ |
| MWH_MTH06NUMVALID | 1877 | 0.88 | 137.28 | 297.43 | 0 | 18 | 55.0 | 144.00 | 8009 | ▇▁▁▁▁ |
| MWH_MTH07NUMVALID | 2251 | 0.86 | 140.82 | 298.64 | 0 | 19 | 58.0 | 149.00 | 8077 | ▇▁▁▁▁ |
| MWH_MTH08NUMVALID | 2281 | 0.86 | 144.13 | 304.19 | 0 | 20 | 59.0 | 152.00 | 8123 | ▇▁▁▁▁ |
| MWH_MTHHSNUMVALID | 3955 | 0.75 | 186.51 | 516.44 | 0 | 26 | 75.0 | 183.00 | 22170 | ▇▁▁▁▁ |
| M_MTH00NUMVALID | 28 | 1.00 | 834.15 | 3254.34 | 0 | 77 | 229.0 | 661.00 | 181033 | ▇▁▁▁▁ |
| M_MTH03NUMVALID | 1428 | 0.91 | 131.39 | 494.58 | 0 | 14 | 38.0 | 104.00 | 26338 | ▇▁▁▁▁ |
| M_MTH04NUMVALID | 1418 | 0.91 | 131.16 | 487.66 | 0 | 14 | 38.0 | 104.00 | 26349 | ▇▁▁▁▁ |
| M_MTH05NUMVALID | 1412 | 0.91 | 129.37 | 479.55 | 0 | 14 | 39.0 | 104.00 | 26324 | ▇▁▁▁▁ |
| M_MTH06NUMVALID | 1410 | 0.91 | 128.48 | 471.16 | 0 | 14 | 39.0 | 105.00 | 25317 | ▇▁▁▁▁ |
| M_MTH07NUMVALID | 1830 | 0.88 | 131.15 | 479.92 | 0 | 15 | 41.0 | 108.00 | 25369 | ▇▁▁▁▁ |
| M_MTH08NUMVALID | 1891 | 0.88 | 132.66 | 475.00 | 0 | 16 | 42.0 | 111.00 | 25786 | ▇▁▁▁▁ |
| M_MTHHSNUMVALID | 3712 | 0.76 | 165.46 | 647.22 | 0 | 20 | 51.0 | 127.00 | 25550 | ▇▁▁▁▁ |
| YEAR | 0 | 1.00 | 2010.00 | 0.00 | 2010 | 2010 | 2010.0 | 2010.00 | 2010 | ▁▁▇▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DL_INGESTION_DATETIME | 0 | 1 | 2021-08-20 16:11:49 | 2021-08-20 16:11:49 | 2021-08-20 16:11:49 | 1 |
skim(rlalea_10)
| Name | rlalea_10 |
| Number of rows | 15717 |
| Number of columns | 232 |
| _______________________ | |
| Column type frequency: | |
| character | 118 |
| numeric | 113 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ALL_RLA00PCTPROF | 0 | 1.00 | 1 | 5 | 0 | 132 | 0 |
| ALL_RLA03PCTPROF | 1339 | 0.91 | 2 | 5 | 0 | 123 | 0 |
| ALL_RLA04PCTPROF | 1342 | 0.91 | 2 | 5 | 0 | 117 | 0 |
| ALL_RLA05PCTPROF | 1335 | 0.92 | 2 | 5 | 0 | 118 | 0 |
| ALL_RLA06PCTPROF | 1333 | 0.92 | 2 | 5 | 0 | 116 | 0 |
| ALL_RLA07PCTPROF | 1770 | 0.89 | 2 | 5 | 0 | 117 | 0 |
| ALL_RLA08PCTPROF | 1818 | 0.88 | 2 | 5 | 0 | 117 | 0 |
| ALL_RLAHSPCTPROF | 3683 | 0.77 | 2 | 5 | 0 | 118 | 0 |
| CWD_RLA00PCTPROF | 187 | 0.99 | 1 | 5 | 0 | 127 | 0 |
| CWD_RLA03PCTPROF | 1996 | 0.87 | 2 | 5 | 0 | 118 | 0 |
| CWD_RLA04PCTPROF | 1986 | 0.87 | 1 | 5 | 0 | 109 | 0 |
| CWD_RLA05PCTPROF | 1994 | 0.87 | 1 | 5 | 0 | 117 | 0 |
| CWD_RLA06PCTPROF | 2018 | 0.87 | 1 | 5 | 0 | 109 | 0 |
| CWD_RLA07PCTPROF | 2374 | 0.85 | 1 | 5 | 0 | 102 | 0 |
| CWD_RLA08PCTPROF | 2448 | 0.84 | 2 | 5 | 0 | 101 | 0 |
| CWD_RLAHSPCTPROF | 4133 | 0.74 | 1 | 5 | 0 | 99 | 0 |
| ECD_RLA00PCTPROF | 401 | 0.97 | 1 | 5 | 0 | 123 | 0 |
| ECD_RLA03PCTPROF | 1766 | 0.89 | 2 | 5 | 0 | 121 | 0 |
| ECD_RLA04PCTPROF | 1749 | 0.89 | 2 | 5 | 0 | 115 | 0 |
| ECD_RLA05PCTPROF | 1731 | 0.89 | 2 | 5 | 0 | 114 | 0 |
| ECD_RLA06PCTPROF | 1749 | 0.89 | 2 | 5 | 0 | 114 | 0 |
| ECD_RLA07PCTPROF | 2155 | 0.86 | 2 | 5 | 0 | 113 | 0 |
| ECD_RLA08PCTPROF | 2198 | 0.86 | 2 | 5 | 0 | 120 | 0 |
| ECD_RLAHSPCTPROF | 3966 | 0.75 | 2 | 5 | 0 | 119 | 0 |
| FIPST | 0 | 1.00 | 2 | 2 | 0 | 51 | 0 |
| F_RLA00PCTPROF | 40 | 1.00 | 2 | 5 | 0 | 114 | 0 |
| F_RLA03PCTPROF | 1433 | 0.91 | 2 | 5 | 0 | 118 | 0 |
| F_RLA04PCTPROF | 1441 | 0.91 | 2 | 5 | 0 | 108 | 0 |
| F_RLA05PCTPROF | 1442 | 0.91 | 2 | 5 | 0 | 104 | 0 |
| F_RLA06PCTPROF | 1440 | 0.91 | 2 | 5 | 0 | 105 | 0 |
| F_RLA07PCTPROF | 1866 | 0.88 | 2 | 5 | 0 | 105 | 0 |
| F_RLA08PCTPROF | 1916 | 0.88 | 2 | 5 | 0 | 106 | 0 |
| F_RLAHSPCTPROF | 3752 | 0.76 | 2 | 5 | 0 | 108 | 0 |
| FILEURL | 0 | 1.00 | 87 | 87 | 0 | 1 | 0 |
| HOM_RLA00PCTPROF | 9576 | 0.39 | 2 | 5 | 0 | 94 | 0 |
| HOM_RLA03PCTPROF | 11027 | 0.30 | 2 | 5 | 0 | 42 | 0 |
| HOM_RLA04PCTPROF | 11079 | 0.30 | 2 | 5 | 0 | 41 | 0 |
| HOM_RLA05PCTPROF | 11078 | 0.30 | 2 | 5 | 0 | 40 | 0 |
| HOM_RLA06PCTPROF | 11164 | 0.29 | 2 | 5 | 0 | 39 | 0 |
| HOM_RLA07PCTPROF | 11285 | 0.28 | 2 | 5 | 0 | 38 | 0 |
| HOM_RLA08PCTPROF | 11324 | 0.28 | 2 | 5 | 0 | 40 | 0 |
| HOM_RLAHSPCTPROF | 11942 | 0.24 | 2 | 5 | 0 | 44 | 0 |
| LEAID | 0 | 1.00 | 7 | 7 | 0 | 15717 | 0 |
| LEANM09 | 0 | 1.00 | 3 | 60 | 0 | 15436 | 0 |
| LEP_RLA00PCTPROF | 4442 | 0.72 | 1 | 5 | 0 | 125 | 0 |
| LEP_RLA03PCTPROF | 6479 | 0.59 | 1 | 5 | 0 | 119 | 0 |
| LEP_RLA04PCTPROF | 6656 | 0.58 | 1 | 5 | 0 | 109 | 0 |
| LEP_RLA05PCTPROF | 6902 | 0.56 | 1 | 5 | 0 | 110 | 0 |
| LEP_RLA06PCTPROF | 7103 | 0.55 | 1 | 5 | 0 | 107 | 0 |
| LEP_RLA07PCTPROF | 7462 | 0.53 | 1 | 5 | 0 | 99 | 0 |
| LEP_RLA08PCTPROF | 7576 | 0.52 | 1 | 5 | 0 | 94 | 0 |
| LEP_RLAHSPCTPROF | 8855 | 0.44 | 1 | 5 | 0 | 84 | 0 |
| MAM_RLA00PCTPROF | 4231 | 0.73 | 2 | 5 | 0 | 98 | 0 |
| MAM_RLA03PCTPROF | 8186 | 0.48 | 2 | 5 | 0 | 43 | 0 |
| MAM_RLA04PCTPROF | 8196 | 0.48 | 2 | 5 | 0 | 41 | 0 |
| MAM_RLA05PCTPROF | 8182 | 0.48 | 2 | 5 | 0 | 38 | 0 |
| MAM_RLA06PCTPROF | 8120 | 0.48 | 2 | 5 | 0 | 41 | 0 |
| MAM_RLA07PCTPROF | 8294 | 0.47 | 2 | 5 | 0 | 40 | 0 |
| MAM_RLA08PCTPROF | 8276 | 0.47 | 2 | 5 | 0 | 38 | 0 |
| MAM_RLAHSPCTPROF | 9114 | 0.42 | 2 | 5 | 0 | 40 | 0 |
| MAS_RLA00PCTPROF | 3360 | 0.79 | 2 | 5 | 0 | 96 | 0 |
| MAS_RLA03PCTPROF | 6857 | 0.56 | 2 | 5 | 0 | 81 | 0 |
| MAS_RLA04PCTPROF | 6853 | 0.56 | 2 | 5 | 0 | 71 | 0 |
| MAS_RLA05PCTPROF | 6889 | 0.56 | 2 | 5 | 0 | 74 | 0 |
| MAS_RLA06PCTPROF | 6939 | 0.56 | 2 | 5 | 0 | 76 | 0 |
| MAS_RLA07PCTPROF | 7174 | 0.54 | 2 | 5 | 0 | 76 | 0 |
| MAS_RLA08PCTPROF | 7188 | 0.54 | 2 | 5 | 0 | 74 | 0 |
| MAS_RLAHSPCTPROF | 8130 | 0.48 | 2 | 5 | 0 | 80 | 0 |
| MBL_RLA00PCTPROF | 1962 | 0.88 | 2 | 5 | 0 | 122 | 0 |
| MBL_RLA03PCTPROF | 5449 | 0.65 | 2 | 5 | 0 | 116 | 0 |
| MBL_RLA04PCTPROF | 5362 | 0.66 | 2 | 5 | 0 | 112 | 0 |
| MBL_RLA05PCTPROF | 5311 | 0.66 | 2 | 5 | 0 | 115 | 0 |
| MBL_RLA06PCTPROF | 5307 | 0.66 | 2 | 5 | 0 | 113 | 0 |
| MBL_RLA07PCTPROF | 5649 | 0.64 | 2 | 5 | 0 | 113 | 0 |
| MBL_RLA08PCTPROF | 5681 | 0.64 | 2 | 5 | 0 | 117 | 0 |
| MBL_RLAHSPCTPROF | 6844 | 0.56 | 2 | 5 | 0 | 118 | 0 |
| MHI_RLA00PCTPROF | 1357 | 0.91 | 2 | 5 | 0 | 117 | 0 |
| MHI_RLA03PCTPROF | 4127 | 0.74 | 2 | 5 | 0 | 120 | 0 |
| MHI_RLA04PCTPROF | 4222 | 0.73 | 2 | 5 | 0 | 109 | 0 |
| MHI_RLA05PCTPROF | 4208 | 0.73 | 2 | 5 | 0 | 106 | 0 |
| MHI_RLA06PCTPROF | 4278 | 0.73 | 2 | 5 | 0 | 107 | 0 |
| MHI_RLA07PCTPROF | 4551 | 0.71 | 2 | 5 | 0 | 106 | 0 |
| MHI_RLA08PCTPROF | 4683 | 0.70 | 2 | 5 | 0 | 115 | 0 |
| MHI_RLAHSPCTPROF | 6085 | 0.61 | 2 | 5 | 0 | 110 | 0 |
| MIG_RLA00PCTPROF | 10414 | 0.34 | 2 | 5 | 0 | 76 | 0 |
| MIG_RLA03PCTPROF | 11733 | 0.25 | 2 | 5 | 0 | 39 | 0 |
| MIG_RLA04PCTPROF | 11748 | 0.25 | 2 | 5 | 0 | 32 | 0 |
| MIG_RLA05PCTPROF | 11765 | 0.25 | 2 | 5 | 0 | 33 | 0 |
| MIG_RLA06PCTPROF | 11817 | 0.25 | 2 | 5 | 0 | 30 | 0 |
| MIG_RLA07PCTPROF | 11972 | 0.24 | 2 | 5 | 0 | 33 | 0 |
| MIG_RLA08PCTPROF | 11969 | 0.24 | 2 | 5 | 0 | 35 | 0 |
| MIG_RLAHSPCTPROF | 12556 | 0.20 | 2 | 5 | 0 | 38 | 0 |
| MTR_RLA00PCTPROF | 8836 | 0.44 | 2 | 5 | 0 | 91 | 0 |
| MTR_RLA03PCTPROF | 10319 | 0.34 | 2 | 5 | 0 | 45 | 0 |
| MTR_RLA04PCTPROF | 10417 | 0.34 | 2 | 5 | 0 | 39 | 0 |
| MTR_RLA05PCTPROF | 10409 | 0.34 | 2 | 5 | 0 | 35 | 0 |
| MTR_RLA06PCTPROF | 10510 | 0.33 | 2 | 5 | 0 | 36 | 0 |
| MTR_RLA07PCTPROF | 10696 | 0.32 | 2 | 5 | 0 | 40 | 0 |
| MTR_RLA08PCTPROF | 10732 | 0.32 | 2 | 5 | 0 | 39 | 0 |
| MTR_RLAHSPCTPROF | 11549 | 0.27 | 2 | 5 | 0 | 48 | 0 |
| MWH_RLA00PCTPROF | 421 | 0.97 | 2 | 5 | 0 | 109 | 0 |
| MWH_RLA03PCTPROF | 1861 | 0.88 | 2 | 5 | 0 | 100 | 0 |
| MWH_RLA04PCTPROF | 1865 | 0.88 | 2 | 5 | 0 | 103 | 0 |
| MWH_RLA05PCTPROF | 1895 | 0.88 | 2 | 5 | 0 | 98 | 0 |
| MWH_RLA06PCTPROF | 1853 | 0.88 | 2 | 5 | 0 | 96 | 0 |
| MWH_RLA07PCTPROF | 2243 | 0.86 | 2 | 5 | 0 | 99 | 0 |
| MWH_RLA08PCTPROF | 2264 | 0.86 | 2 | 5 | 0 | 103 | 0 |
| MWH_RLAHSPCTPROF | 3967 | 0.75 | 2 | 5 | 0 | 101 | 0 |
| M_RLA00PCTPROF | 24 | 1.00 | 1 | 5 | 0 | 122 | 0 |
| M_RLA03PCTPROF | 1399 | 0.91 | 2 | 5 | 0 | 120 | 0 |
| M_RLA04PCTPROF | 1392 | 0.91 | 2 | 5 | 0 | 116 | 0 |
| M_RLA05PCTPROF | 1390 | 0.91 | 2 | 5 | 0 | 114 | 0 |
| M_RLA06PCTPROF | 1390 | 0.91 | 2 | 5 | 0 | 116 | 0 |
| M_RLA07PCTPROF | 1819 | 0.88 | 2 | 5 | 0 | 117 | 0 |
| M_RLA08PCTPROF | 1876 | 0.88 | 2 | 5 | 0 | 117 | 0 |
| M_RLAHSPCTPROF | 3725 | 0.76 | 2 | 5 | 0 | 116 | 0 |
| STNAM | 0 | 1.00 | 4 | 24 | 0 | 51 | 0 |
| PIPELINE | 0 | 1.00 | 37 | 37 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ALL_RLA00NUMVALID | 0 | 1.00 | 1619.22 | 6341.20 | 0 | 150 | 445.0 | 1280.00 | 354300 | ▇▁▁▁▁ |
| ALL_RLA03NUMVALID | 1339 | 0.91 | 254.95 | 959.86 | 0 | 28 | 74.0 | 200.00 | 51708 | ▇▁▁▁▁ |
| ALL_RLA04NUMVALID | 1342 | 0.91 | 254.90 | 950.06 | 0 | 28 | 74.0 | 202.00 | 51714 | ▇▁▁▁▁ |
| ALL_RLA05NUMVALID | 1335 | 0.92 | 251.72 | 937.02 | 0 | 28 | 75.0 | 203.00 | 51447 | ▇▁▁▁▁ |
| ALL_RLA06NUMVALID | 1333 | 0.92 | 250.40 | 919.39 | 0 | 28 | 76.0 | 205.00 | 49186 | ▇▁▁▁▁ |
| ALL_RLA07NUMVALID | 1770 | 0.89 | 257.32 | 940.54 | 0 | 30 | 80.0 | 212.00 | 49820 | ▇▁▁▁▁ |
| ALL_RLA08NUMVALID | 1818 | 0.88 | 258.98 | 933.11 | 0 | 30 | 81.0 | 214.00 | 50401 | ▇▁▁▁▁ |
| ALL_RLAHSNUMVALID | 3683 | 0.77 | 308.21 | 1190.43 | 0 | 38 | 97.0 | 242.00 | 50027 | ▇▁▁▁▁ |
| CWD_RLA00NUMVALID | 187 | 0.99 | 213.74 | 882.36 | 0 | 21 | 60.0 | 174.00 | 58700 | ▇▁▁▁▁ |
| CWD_RLA03NUMVALID | 1996 | 0.87 | 35.38 | 138.53 | 0 | 4 | 11.0 | 29.00 | 9298 | ▇▁▁▁▁ |
| CWD_RLA04NUMVALID | 1986 | 0.87 | 36.67 | 143.98 | 0 | 4 | 11.0 | 30.00 | 9681 | ▇▁▁▁▁ |
| CWD_RLA05NUMVALID | 1994 | 0.87 | 36.30 | 142.77 | 0 | 4 | 11.0 | 30.00 | 9305 | ▇▁▁▁▁ |
| CWD_RLA06NUMVALID | 2018 | 0.87 | 35.11 | 136.29 | 0 | 4 | 11.0 | 29.00 | 8980 | ▇▁▁▁▁ |
| CWD_RLA07NUMVALID | 2374 | 0.85 | 34.99 | 139.13 | 0 | 4 | 11.0 | 29.00 | 9404 | ▇▁▁▁▁ |
| CWD_RLA08NUMVALID | 2448 | 0.84 | 35.00 | 130.36 | 0 | 4 | 12.0 | 30.00 | 7758 | ▇▁▁▁▁ |
| CWD_RLAHSNUMVALID | 4133 | 0.74 | 36.27 | 134.64 | 0 | 5 | 12.0 | 30.00 | 5096 | ▇▁▁▁▁ |
| ECD_RLA00NUMVALID | 401 | 0.97 | 829.43 | 4415.33 | 0 | 64 | 182.0 | 511.00 | 292359 | ▇▁▁▁▁ |
| ECD_RLA03NUMVALID | 1766 | 0.89 | 141.80 | 705.93 | 0 | 12 | 33.0 | 88.00 | 43268 | ▇▁▁▁▁ |
| ECD_RLA04NUMVALID | 1749 | 0.89 | 139.17 | 690.60 | 0 | 12 | 33.0 | 87.00 | 43213 | ▇▁▁▁▁ |
| ECD_RLA05NUMVALID | 1731 | 0.89 | 135.21 | 675.88 | 0 | 12 | 32.0 | 86.00 | 42939 | ▇▁▁▁▁ |
| ECD_RLA06NUMVALID | 1749 | 0.89 | 131.70 | 656.48 | 0 | 12 | 32.0 | 86.00 | 41010 | ▇▁▁▁▁ |
| ECD_RLA07NUMVALID | 2155 | 0.86 | 131.24 | 662.80 | 0 | 13 | 33.0 | 86.00 | 41048 | ▇▁▁▁▁ |
| ECD_RLA08NUMVALID | 2198 | 0.86 | 128.42 | 645.67 | 0 | 12 | 31.0 | 83.00 | 41069 | ▇▁▁▁▁ |
| ECD_RLAHSNUMVALID | 3966 | 0.75 | 130.61 | 707.05 | 0 | 14 | 32.0 | 81.00 | 39812 | ▇▁▁▁▁ |
| F_RLA00NUMVALID | 40 | 1.00 | 791.94 | 3108.78 | 0 | 73 | 219.0 | 622.00 | 173641 | ▇▁▁▁▁ |
| F_RLA03NUMVALID | 1433 | 0.91 | 124.80 | 468.57 | 0 | 14 | 37.0 | 98.00 | 25419 | ▇▁▁▁▁ |
| F_RLA04NUMVALID | 1441 | 0.91 | 124.92 | 465.21 | 0 | 14 | 37.0 | 99.00 | 25380 | ▇▁▁▁▁ |
| F_RLA05NUMVALID | 1442 | 0.91 | 123.40 | 459.09 | 0 | 14 | 37.0 | 100.00 | 25179 | ▇▁▁▁▁ |
| F_RLA06NUMVALID | 1440 | 0.91 | 122.83 | 449.91 | 0 | 14 | 38.0 | 100.00 | 23918 | ▇▁▁▁▁ |
| F_RLA07NUMVALID | 1866 | 0.88 | 126.15 | 459.87 | 0 | 15 | 40.0 | 104.00 | 24487 | ▇▁▁▁▁ |
| F_RLA08NUMVALID | 1916 | 0.88 | 127.14 | 458.45 | 0 | 15 | 40.0 | 105.00 | 24581 | ▇▁▁▁▁ |
| F_RLAHSNUMVALID | 3752 | 0.76 | 153.11 | 594.10 | 0 | 19 | 48.0 | 120.00 | 24819 | ▇▁▁▁▁ |
| HOM_RLA00NUMVALID | 9576 | 0.39 | 36.96 | 176.55 | 0 | 0 | 3.0 | 15.00 | 5967 | ▇▁▁▁▁ |
| HOM_RLA03NUMVALID | 11027 | 0.30 | 8.34 | 33.04 | 0 | 0 | 1.0 | 4.00 | 990 | ▇▁▁▁▁ |
| HOM_RLA04NUMVALID | 11079 | 0.30 | 7.92 | 31.23 | 0 | 0 | 1.0 | 4.00 | 811 | ▇▁▁▁▁ |
| HOM_RLA05NUMVALID | 11078 | 0.30 | 7.47 | 30.26 | 0 | 0 | 1.0 | 4.00 | 878 | ▇▁▁▁▁ |
| HOM_RLA06NUMVALID | 11164 | 0.29 | 7.09 | 29.13 | 0 | 0 | 1.0 | 4.00 | 853 | ▇▁▁▁▁ |
| HOM_RLA07NUMVALID | 11285 | 0.28 | 6.80 | 29.23 | 0 | 0 | 1.0 | 4.00 | 906 | ▇▁▁▁▁ |
| HOM_RLA08NUMVALID | 11324 | 0.28 | 6.44 | 28.86 | 0 | 0 | 1.0 | 3.00 | 965 | ▇▁▁▁▁ |
| HOM_RLAHSNUMVALID | 11942 | 0.24 | 6.82 | 33.73 | 0 | 0 | 1.0 | 4.00 | 1091 | ▇▁▁▁▁ |
| LEP_RLA00NUMVALID | 4442 | 0.72 | 188.13 | 1262.64 | 0 | 2 | 10.0 | 56.00 | 94962 | ▇▁▁▁▁ |
| LEP_RLA03NUMVALID | 6479 | 0.59 | 53.05 | 316.84 | 0 | 1 | 4.0 | 18.00 | 20195 | ▇▁▁▁▁ |
| LEP_RLA04NUMVALID | 6656 | 0.58 | 44.83 | 269.53 | 0 | 1 | 3.0 | 16.00 | 17209 | ▇▁▁▁▁ |
| LEP_RLA05NUMVALID | 6902 | 0.56 | 36.41 | 213.16 | 0 | 1 | 3.0 | 13.00 | 13948 | ▇▁▁▁▁ |
| LEP_RLA06NUMVALID | 7103 | 0.55 | 30.46 | 170.11 | 0 | 1 | 3.0 | 12.00 | 10814 | ▇▁▁▁▁ |
| LEP_RLA07NUMVALID | 7462 | 0.53 | 28.96 | 167.96 | 0 | 1 | 3.0 | 11.00 | 10996 | ▇▁▁▁▁ |
| LEP_RLA08NUMVALID | 7576 | 0.52 | 26.81 | 161.03 | 0 | 1 | 2.0 | 10.00 | 10893 | ▇▁▁▁▁ |
| LEP_RLAHSNUMVALID | 8855 | 0.44 | 26.85 | 179.57 | 0 | 0 | 2.0 | 9.00 | 10907 | ▇▁▁▁▁ |
| MAM_RLA00NUMVALID | 4231 | 0.73 | 27.49 | 118.97 | 0 | 1 | 4.0 | 13.00 | 5481 | ▇▁▁▁▁ |
| MAM_RLA03NUMVALID | 8186 | 0.48 | 6.08 | 21.75 | 0 | 0 | 1.0 | 4.00 | 846 | ▇▁▁▁▁ |
| MAM_RLA04NUMVALID | 8196 | 0.48 | 6.09 | 22.35 | 0 | 0 | 1.0 | 4.00 | 930 | ▇▁▁▁▁ |
| MAM_RLA05NUMVALID | 8182 | 0.48 | 6.07 | 21.69 | 0 | 0 | 1.0 | 4.00 | 843 | ▇▁▁▁▁ |
| MAM_RLA06NUMVALID | 8120 | 0.48 | 5.96 | 20.52 | 0 | 0 | 1.0 | 4.00 | 796 | ▇▁▁▁▁ |
| MAM_RLA07NUMVALID | 8294 | 0.47 | 6.01 | 20.35 | 0 | 0 | 1.0 | 4.00 | 750 | ▇▁▁▁▁ |
| MAM_RLA08NUMVALID | 8276 | 0.47 | 5.89 | 20.09 | 0 | 0 | 1.0 | 4.00 | 738 | ▇▁▁▁▁ |
| MAM_RLAHSNUMVALID | 9114 | 0.42 | 6.76 | 23.98 | 0 | 0 | 1.0 | 4.00 | 782 | ▇▁▁▁▁ |
| MAS_RLA00NUMVALID | 3360 | 0.79 | 103.96 | 863.16 | 0 | 2 | 6.0 | 28.00 | 74473 | ▇▁▁▁▁ |
| MAS_RLA03NUMVALID | 6857 | 0.56 | 21.02 | 145.69 | 0 | 1 | 2.0 | 8.00 | 10786 | ▇▁▁▁▁ |
| MAS_RLA04NUMVALID | 6853 | 0.56 | 21.32 | 150.02 | 0 | 1 | 2.0 | 8.00 | 11155 | ▇▁▁▁▁ |
| MAS_RLA05NUMVALID | 6889 | 0.56 | 20.27 | 145.00 | 0 | 1 | 2.0 | 8.00 | 10818 | ▇▁▁▁▁ |
| MAS_RLA06NUMVALID | 6939 | 0.56 | 20.43 | 144.87 | 0 | 1 | 2.0 | 8.00 | 10588 | ▇▁▁▁▁ |
| MAS_RLA07NUMVALID | 7174 | 0.54 | 21.26 | 146.45 | 0 | 1 | 2.0 | 8.00 | 10240 | ▇▁▁▁▁ |
| MAS_RLA08NUMVALID | 7188 | 0.54 | 21.32 | 147.73 | 0 | 1 | 2.0 | 8.00 | 10306 | ▇▁▁▁▁ |
| MAS_RLAHSNUMVALID | 8130 | 0.48 | 24.73 | 165.73 | 0 | 1 | 2.0 | 8.00 | 10580 | ▇▁▁▁▁ |
| MBL_RLA00NUMVALID | 1962 | 0.88 | 293.97 | 1840.98 | 0 | 3 | 13.0 | 91.00 | 87980 | ▇▁▁▁▁ |
| MBL_RLA03NUMVALID | 5449 | 0.65 | 57.02 | 311.92 | 0 | 1 | 4.0 | 24.00 | 13865 | ▇▁▁▁▁ |
| MBL_RLA04NUMVALID | 5362 | 0.66 | 56.73 | 301.63 | 0 | 1 | 4.0 | 24.00 | 12579 | ▇▁▁▁▁ |
| MBL_RLA05NUMVALID | 5311 | 0.66 | 55.35 | 296.99 | 0 | 1 | 4.0 | 24.00 | 12641 | ▇▁▁▁▁ |
| MBL_RLA06NUMVALID | 5307 | 0.66 | 55.42 | 295.19 | 0 | 1 | 4.0 | 24.00 | 13179 | ▇▁▁▁▁ |
| MBL_RLA07NUMVALID | 5649 | 0.64 | 56.54 | 297.15 | 0 | 1 | 4.0 | 25.00 | 12789 | ▇▁▁▁▁ |
| MBL_RLA08NUMVALID | 5681 | 0.64 | 56.73 | 297.96 | 0 | 1 | 4.0 | 25.00 | 13004 | ▇▁▁▁▁ |
| MBL_RLAHSNUMVALID | 6844 | 0.56 | 65.27 | 371.34 | 0 | 1 | 4.0 | 27.00 | 14365 | ▇▁▁▁▁ |
| MHI_RLA00NUMVALID | 1357 | 0.91 | 406.41 | 3795.81 | 0 | 5 | 21.0 | 104.00 | 261952 | ▇▁▁▁▁ |
| MHI_RLA03NUMVALID | 4127 | 0.74 | 76.85 | 618.33 | 0 | 1 | 5.0 | 24.00 | 38267 | ▇▁▁▁▁ |
| MHI_RLA04NUMVALID | 4222 | 0.73 | 75.69 | 621.80 | 0 | 1 | 5.0 | 23.00 | 38633 | ▇▁▁▁▁ |
| MHI_RLA05NUMVALID | 4208 | 0.73 | 73.77 | 612.11 | 0 | 1 | 5.0 | 23.00 | 38276 | ▇▁▁▁▁ |
| MHI_RLA06NUMVALID | 4278 | 0.73 | 72.55 | 603.84 | 0 | 1 | 5.0 | 23.00 | 38702 | ▇▁▁▁▁ |
| MHI_RLA07NUMVALID | 4551 | 0.71 | 73.20 | 621.94 | 0 | 1 | 5.0 | 23.00 | 40489 | ▇▁▁▁▁ |
| MHI_RLA08NUMVALID | 4683 | 0.70 | 72.85 | 608.43 | 0 | 1 | 5.0 | 23.00 | 37511 | ▇▁▁▁▁ |
| MHI_RLAHSNUMVALID | 6085 | 0.61 | 80.49 | 677.09 | 0 | 1 | 5.0 | 24.00 | 36925 | ▇▁▁▁▁ |
| MIG_RLA00NUMVALID | 10414 | 0.34 | 24.68 | 105.48 | 0 | 0 | 1.0 | 8.00 | 2570 | ▇▁▁▁▁ |
| MIG_RLA03NUMVALID | 11733 | 0.25 | 5.03 | 18.98 | 0 | 0 | 0.0 | 2.00 | 400 | ▇▁▁▁▁ |
| MIG_RLA04NUMVALID | 11748 | 0.25 | 4.89 | 18.28 | 0 | 0 | 0.0 | 2.00 | 348 | ▇▁▁▁▁ |
| MIG_RLA05NUMVALID | 11765 | 0.25 | 4.85 | 18.19 | 0 | 0 | 0.0 | 2.00 | 356 | ▇▁▁▁▁ |
| MIG_RLA06NUMVALID | 11817 | 0.25 | 4.78 | 17.74 | 0 | 0 | 0.0 | 2.00 | 357 | ▇▁▁▁▁ |
| MIG_RLA07NUMVALID | 11972 | 0.24 | 4.90 | 18.45 | 0 | 0 | 0.0 | 2.00 | 392 | ▇▁▁▁▁ |
| MIG_RLA08NUMVALID | 11969 | 0.24 | 4.80 | 18.13 | 0 | 0 | 0.0 | 2.00 | 379 | ▇▁▁▁▁ |
| MIG_RLAHSNUMVALID | 12556 | 0.20 | 5.46 | 22.90 | 0 | 0 | 0.0 | 2.00 | 375 | ▇▁▁▁▁ |
| MTR_RLA00NUMVALID | 8836 | 0.44 | 44.81 | 171.75 | 0 | 2 | 7.0 | 28.00 | 6302 | ▇▁▁▁▁ |
| MTR_RLA03NUMVALID | 10319 | 0.34 | 9.63 | 32.58 | 0 | 0 | 2.0 | 7.00 | 806 | ▇▁▁▁▁ |
| MTR_RLA04NUMVALID | 10417 | 0.34 | 9.07 | 30.05 | 0 | 0 | 2.0 | 7.00 | 880 | ▇▁▁▁▁ |
| MTR_RLA05NUMVALID | 10409 | 0.34 | 8.56 | 29.77 | 0 | 0 | 2.0 | 6.00 | 1045 | ▇▁▁▁▁ |
| MTR_RLA06NUMVALID | 10510 | 0.33 | 8.29 | 28.61 | 0 | 0 | 2.0 | 6.00 | 1028 | ▇▁▁▁▁ |
| MTR_RLA07NUMVALID | 10696 | 0.32 | 8.11 | 27.93 | 0 | 0 | 2.0 | 6.00 | 1005 | ▇▁▁▁▁ |
| MTR_RLA08NUMVALID | 10732 | 0.32 | 7.85 | 27.28 | 0 | 0 | 2.0 | 6.00 | 977 | ▇▁▁▁▁ |
| MTR_RLAHSNUMVALID | 11549 | 0.27 | 9.55 | 46.52 | 0 | 0 | 2.0 | 6.00 | 1880 | ▇▁▁▁▁ |
| MWH_RLA00NUMVALID | 421 | 0.97 | 884.81 | 2103.53 | 0 | 88 | 319.0 | 901.25 | 56235 | ▇▁▁▁▁ |
| MWH_RLA03NUMVALID | 1861 | 0.88 | 136.04 | 303.28 | 0 | 17 | 54.0 | 140.00 | 8099 | ▇▁▁▁▁ |
| MWH_RLA04NUMVALID | 1865 | 0.88 | 137.51 | 305.06 | 0 | 18 | 55.0 | 143.00 | 8122 | ▇▁▁▁▁ |
| MWH_RLA05NUMVALID | 1895 | 0.88 | 137.95 | 304.15 | 0 | 18 | 55.0 | 143.00 | 8173 | ▇▁▁▁▁ |
| MWH_RLA06NUMVALID | 1853 | 0.88 | 137.73 | 299.82 | 0 | 18 | 55.5 | 145.00 | 8013 | ▇▁▁▁▁ |
| MWH_RLA07NUMVALID | 2243 | 0.86 | 142.37 | 305.10 | 0 | 19 | 59.0 | 150.00 | 8081 | ▇▁▁▁▁ |
| MWH_RLA08NUMVALID | 2264 | 0.86 | 144.59 | 308.40 | 0 | 20 | 59.0 | 152.00 | 8171 | ▇▁▁▁▁ |
| MWH_RLAHSNUMVALID | 3967 | 0.75 | 175.70 | 441.50 | 0 | 26 | 73.0 | 178.00 | 12213 | ▇▁▁▁▁ |
| M_RLA00NUMVALID | 24 | 1.00 | 829.02 | 3236.33 | 0 | 77 | 229.0 | 657.00 | 180650 | ▇▁▁▁▁ |
| M_RLA03NUMVALID | 1399 | 0.91 | 131.21 | 493.41 | 0 | 14 | 38.0 | 104.00 | 26286 | ▇▁▁▁▁ |
| M_RLA04NUMVALID | 1392 | 0.91 | 131.01 | 486.85 | 0 | 14 | 38.0 | 104.00 | 26333 | ▇▁▁▁▁ |
| M_RLA05NUMVALID | 1390 | 0.91 | 129.47 | 480.15 | 0 | 14 | 39.0 | 104.00 | 26267 | ▇▁▁▁▁ |
| M_RLA06NUMVALID | 1390 | 0.91 | 128.73 | 471.69 | 0 | 14 | 39.0 | 105.00 | 25267 | ▇▁▁▁▁ |
| M_RLA07NUMVALID | 1819 | 0.88 | 132.24 | 482.73 | 0 | 16 | 41.0 | 108.00 | 25333 | ▇▁▁▁▁ |
| M_RLA08NUMVALID | 1876 | 0.88 | 133.03 | 476.86 | 0 | 16 | 42.0 | 111.00 | 25819 | ▇▁▁▁▁ |
| M_RLAHSNUMVALID | 3725 | 0.76 | 156.44 | 599.42 | 0 | 20 | 50.0 | 123.00 | 25345 | ▇▁▁▁▁ |
| YEAR | 0 | 1.00 | 2010.00 | 0.00 | 2010 | 2010 | 2010.0 | 2010.00 | 2010 | ▁▁▇▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DL_INGESTION_DATETIME | 0 | 1 | 2021-08-20 16:11:49 | 2021-08-20 16:11:49 | 2021-08-20 16:11:49 | 1 |
mathlea_10 %>% tabyl(ECD_MTHHSPCTPROF)
## ECD_MTHHSPCTPROF n percent valid_percent
## 10 1 6.350416e-05 8.478169e-05
## 10-14 53 3.365720e-03 4.493429e-03
## 11-19 111 7.048962e-03 9.410767e-03
## 12 1 6.350416e-05 8.478169e-05
## 14 4 2.540166e-04 3.391267e-04
## 15 2 1.270083e-04 1.695634e-04
## 15-19 89 5.651870e-03 7.545570e-03
## 16 1 6.350416e-05 8.478169e-05
## 17 2 1.270083e-04 1.695634e-04
## 18 1 6.350416e-05 8.478169e-05
## 19 8 5.080333e-04 6.782535e-04
## 20 2 1.270083e-04 1.695634e-04
## 20-24 133 8.446053e-03 1.127596e-02
## 20-29 176 1.117673e-02 1.492158e-02
## 21 6 3.810250e-04 5.086901e-04
## 21-39 392 2.489363e-02 3.323442e-02
## 22 6 3.810250e-04 5.086901e-04
## 23 7 4.445291e-04 5.934718e-04
## 24 7 4.445291e-04 5.934718e-04
## 25 6 3.810250e-04 5.086901e-04
## 25-29 175 1.111323e-02 1.483680e-02
## 26 12 7.620499e-04 1.017380e-03
## 27 8 5.080333e-04 6.782535e-04
## 28 8 5.080333e-04 6.782535e-04
## 29 4 2.540166e-04 3.391267e-04
## 3 1 6.350416e-05 8.478169e-05
## 30 21 1.333587e-03 1.780415e-03
## 30-34 215 1.365339e-02 1.822806e-02
## 30-39 295 1.873373e-02 2.501060e-02
## 31 12 7.620499e-04 1.017380e-03
## 32 12 7.620499e-04 1.017380e-03
## 33 14 8.890582e-04 1.186944e-03
## 34 18 1.143075e-03 1.526070e-03
## 35 15 9.525624e-04 1.271725e-03
## 35-39 202 1.282784e-02 1.712590e-02
## 36 14 8.890582e-04 1.186944e-03
## 37 19 1.206579e-03 1.610852e-03
## 38 20 1.270083e-03 1.695634e-03
## 39 15 9.525624e-04 1.271725e-03
## 40 18 1.143075e-03 1.526070e-03
## 40-44 226 1.435194e-02 1.916066e-02
## 40-49 280 1.778116e-02 2.373887e-02
## 40-59 582 3.695942e-02 4.934294e-02
## 41 24 1.524100e-03 2.034760e-03
## 42 19 1.206579e-03 1.610852e-03
## 43 15 9.525624e-04 1.271725e-03
## 44 12 7.620499e-04 1.017380e-03
## 45 14 8.890582e-04 1.186944e-03
## 45-49 245 1.555852e-02 2.077151e-02
## 46 14 8.890582e-04 1.186944e-03
## 47 10 6.350416e-04 8.478169e-04
## 48 19 1.206579e-03 1.610852e-03
## 49 19 1.206579e-03 1.610852e-03
## 50 14 8.890582e-04 1.186944e-03
## 50-54 215 1.365339e-02 1.822806e-02
## 50-59 317 2.013082e-02 2.687579e-02
## 51 16 1.016067e-03 1.356507e-03
## 52 11 6.985458e-04 9.325986e-04
## 53 17 1.079571e-03 1.441289e-03
## 54 15 9.525624e-04 1.271725e-03
## 55 15 9.525624e-04 1.271725e-03
## 55-59 211 1.339938e-02 1.788894e-02
## 56 19 1.206579e-03 1.610852e-03
## 57 13 8.255541e-04 1.102162e-03
## 58 18 1.143075e-03 1.526070e-03
## 59 19 1.206579e-03 1.610852e-03
## 6 1 6.350416e-05 8.478169e-05
## 6-9 15 9.525624e-04 1.271725e-03
## 60 18 1.143075e-03 1.526070e-03
## 60-64 250 1.587604e-02 2.119542e-02
## 60-69 358 2.273449e-02 3.035184e-02
## 60-79 658 4.178574e-02 5.578635e-02
## 61 9 5.715374e-04 7.630352e-04
## 62 19 1.206579e-03 1.610852e-03
## 63 18 1.143075e-03 1.526070e-03
## 64 12 7.620499e-04 1.017380e-03
## 65 19 1.206579e-03 1.610852e-03
## 65-69 248 1.574903e-02 2.102586e-02
## 66 12 7.620499e-04 1.017380e-03
## 67 15 9.525624e-04 1.271725e-03
## 68 15 9.525624e-04 1.271725e-03
## 69 12 7.620499e-04 1.017380e-03
## 70 16 1.016067e-03 1.356507e-03
## 70-74 225 1.428844e-02 1.907588e-02
## 70-79 298 1.892424e-02 2.526494e-02
## 71 11 6.985458e-04 9.325986e-04
## 72 14 8.890582e-04 1.186944e-03
## 73 8 5.080333e-04 6.782535e-04
## 74 9 5.715374e-04 7.630352e-04
## 75 9 5.715374e-04 7.630352e-04
## 75-79 168 1.066870e-02 1.424332e-02
## 76 9 5.715374e-04 7.630352e-04
## 77 7 4.445291e-04 5.934718e-04
## 78 11 6.985458e-04 9.325986e-04
## 79 7 4.445291e-04 5.934718e-04
## 8 1 6.350416e-05 8.478169e-05
## 80 3 1.905125e-04 2.543451e-04
## 80-84 109 6.921953e-03 9.241204e-03
## 80-89 244 1.549501e-02 2.068673e-02
## 81 8 5.080333e-04 6.782535e-04
## 82 5 3.175208e-04 4.239084e-04
## 83 5 3.175208e-04 4.239084e-04
## 84 7 4.445291e-04 5.934718e-04
## 85 3 1.905125e-04 2.543451e-04
## 85-89 93 5.905887e-03 7.884697e-03
## 86 5 3.175208e-04 4.239084e-04
## 87 10 6.350416e-04 8.478169e-04
## 88 8 5.080333e-04 6.782535e-04
## 89 11 6.985458e-04 9.325986e-04
## 9 1 6.350416e-05 8.478169e-05
## 90 3 1.905125e-04 2.543451e-04
## 90-94 54 3.429225e-03 4.578211e-03
## 91 4 2.540166e-04 3.391267e-04
## 92 4 2.540166e-04 3.391267e-04
## 93 6 3.810250e-04 5.086901e-04
## 94 3 1.905125e-04 2.543451e-04
## 95 2 1.270083e-04 1.695634e-04
## 96 2 1.270083e-04 1.695634e-04
## 97 1 6.350416e-05 8.478169e-05
## 98 1 6.350416e-05 8.478169e-05
## GE50 1521 9.658983e-02 1.289529e-01
## GE80 456 2.895790e-02 3.866045e-02
## GE90 113 7.175970e-03 9.580331e-03
## GE95 23 1.460596e-03 1.949979e-03
## LE10 58 3.683241e-03 4.917338e-03
## LE20 204 1.295485e-02 1.729546e-02
## LE5 13 8.255541e-04 1.102162e-03
## LT50 817 5.188290e-02 6.926664e-02
## n/a 66 4.191275e-03 5.595591e-03
## PS 975 6.191656e-02 8.266214e-02
## <NA> 3952 2.509684e-01 NA
mathlea_10 %>% tabyl(LEP_MTHHSPCTPROF)
## LEP_MTHHSPCTPROF n percent valid_percent
## 10 1 6.350416e-05 0.0001405679
## 10-14 59 3.746745e-03 0.0082935058
## 11 3 1.905125e-04 0.0004217037
## 11-19 70 4.445291e-03 0.0098397526
## 12 4 2.540166e-04 0.0005622716
## 13 3 1.905125e-04 0.0004217037
## 14 8 5.080333e-04 0.0011245432
## 15 4 2.540166e-04 0.0005622716
## 15-19 61 3.873754e-03 0.0085746416
## 16 5 3.175208e-04 0.0007028395
## 17 4 2.540166e-04 0.0005622716
## 18 2 1.270083e-04 0.0002811358
## 19 7 4.445291e-04 0.0009839753
## 20 5 3.175208e-04 0.0007028395
## 20-24 49 3.111704e-03 0.0068878268
## 20-29 62 3.937258e-03 0.0087152094
## 21 5 3.175208e-04 0.0007028395
## 21-39 122 7.747507e-03 0.0171492831
## 22 6 3.810250e-04 0.0008434074
## 23 1 6.350416e-05 0.0001405679
## 24 2 1.270083e-04 0.0002811358
## 25 5 3.175208e-04 0.0007028395
## 25-29 40 2.540166e-03 0.0056227158
## 26 6 3.810250e-04 0.0008434074
## 28 6 3.810250e-04 0.0008434074
## 29 2 1.270083e-04 0.0002811358
## 30-34 19 1.206579e-03 0.0026707900
## 30-39 48 3.048200e-03 0.0067472589
## 31 3 1.905125e-04 0.0004217037
## 34 2 1.270083e-04 0.0002811358
## 35-39 26 1.651108e-03 0.0036547653
## 36 4 2.540166e-04 0.0005622716
## 37 1 6.350416e-05 0.0001405679
## 39 2 1.270083e-04 0.0002811358
## 40 1 6.350416e-05 0.0001405679
## 40-44 22 1.397092e-03 0.0030924937
## 40-49 33 2.095637e-03 0.0046387405
## 40-59 90 5.715374e-03 0.0126511105
## 42 1 6.350416e-05 0.0001405679
## 44 1 6.350416e-05 0.0001405679
## 45 3 1.905125e-04 0.0004217037
## 45-49 18 1.143075e-03 0.0025302221
## 46 2 1.270083e-04 0.0002811358
## 48 1 6.350416e-05 0.0001405679
## 49 1 6.350416e-05 0.0001405679
## 50-54 21 1.333587e-03 0.0029519258
## 50-59 31 1.968629e-03 0.0043576047
## 53 1 6.350416e-05 0.0001405679
## 55-59 14 8.890582e-04 0.0019679505
## 6 1 6.350416e-05 0.0001405679
## 6-9 26 1.651108e-03 0.0036547653
## 60 1 6.350416e-05 0.0001405679
## 60-64 9 5.715374e-04 0.0012651110
## 60-69 15 9.525624e-04 0.0021085184
## 60-79 48 3.048200e-03 0.0067472589
## 63 1 6.350416e-05 0.0001405679
## 65 1 6.350416e-05 0.0001405679
## 65-69 9 5.715374e-04 0.0012651110
## 66 1 6.350416e-05 0.0001405679
## 7 1 6.350416e-05 0.0001405679
## 70-74 11 6.985458e-04 0.0015462468
## 70-79 16 1.016067e-03 0.0022490863
## 71 2 1.270083e-04 0.0002811358
## 75-79 5 3.175208e-04 0.0007028395
## 8 1 6.350416e-05 0.0001405679
## 80 1 6.350416e-05 0.0001405679
## 80-84 5 3.175208e-04 0.0007028395
## 80-89 4 2.540166e-04 0.0005622716
## 82 1 6.350416e-05 0.0001405679
## 83 1 6.350416e-05 0.0001405679
## 84 1 6.350416e-05 0.0001405679
## 85 1 6.350416e-05 0.0001405679
## 85-89 6 3.810250e-04 0.0008434074
## 87 3 1.905125e-04 0.0004217037
## 9 6 3.810250e-04 0.0008434074
## 90-94 6 3.810250e-04 0.0008434074
## GE50 330 2.095637e-02 0.0463874051
## GE80 34 2.159141e-03 0.0047793084
## GE90 3 1.905125e-04 0.0004217037
## LE10 68 4.318283e-03 0.0095586168
## LE20 171 1.085921e-02 0.0240371099
## LE5 17 1.079571e-03 0.0023896542
## LT50 662 4.203975e-02 0.0930559460
## n/a 1850 1.174827e-01 0.2600506044
## PS 2909 1.847336e-01 0.4089120045
## <NA> 8633 5.482314e-01 NA
mathlea_10 %>% tabyl(HOM_MTHHSPCTPROF)
## HOM_MTHHSPCTPROF n percent valid_percent
## 10 1 6.350416e-05 0.0002473411
## 10-14 1 6.350416e-05 0.0002473411
## 11-19 8 5.080333e-04 0.0019787287
## 15-19 2 1.270083e-04 0.0004946822
## 19 1 6.350416e-05 0.0002473411
## 2 1 6.350416e-05 0.0002473411
## 20-24 2 1.270083e-04 0.0004946822
## 20-29 12 7.620499e-04 0.0029680930
## 21-39 49 3.111704e-03 0.0121197131
## 25-29 5 3.175208e-04 0.0012367054
## 28 1 6.350416e-05 0.0002473411
## 30-34 12 7.620499e-04 0.0029680930
## 30-39 17 1.079571e-03 0.0042047984
## 35-39 6 3.810250e-04 0.0014840465
## 39 1 6.350416e-05 0.0002473411
## 40-44 10 6.350416e-04 0.0024734108
## 40-49 13 8.255541e-04 0.0032154341
## 40-59 52 3.302216e-03 0.0128617363
## 43 1 6.350416e-05 0.0002473411
## 44 1 6.350416e-05 0.0002473411
## 45-49 13 8.255541e-04 0.0032154341
## 49 1 6.350416e-05 0.0002473411
## 50-54 8 5.080333e-04 0.0019787287
## 50-59 15 9.525624e-04 0.0037101163
## 53 1 6.350416e-05 0.0002473411
## 55-59 3 1.905125e-04 0.0007420233
## 6-9 1 6.350416e-05 0.0002473411
## 60-64 3 1.905125e-04 0.0007420233
## 60-69 6 3.810250e-04 0.0014840465
## 60-79 35 2.222646e-03 0.0086569379
## 65-69 3 1.905125e-04 0.0007420233
## 70-74 2 1.270083e-04 0.0004946822
## 70-79 11 6.985458e-04 0.0027207519
## 75-79 1 6.350416e-05 0.0002473411
## 80-84 2 1.270083e-04 0.0004946822
## 80-89 5 3.175208e-04 0.0012367054
## 90-94 1 6.350416e-05 0.0002473411
## GE50 191 1.212929e-02 0.0472421469
## GE80 5 3.175208e-04 0.0012367054
## GE90 2 1.270083e-04 0.0004946822
## LE10 4 2.540166e-04 0.0009893643
## LE20 25 1.587604e-03 0.0061835271
## LE5 1 6.350416e-05 0.0002473411
## LT50 241 1.530450e-02 0.0596092011
## n/a 1819 1.155141e-01 0.4499134306
## PS 1448 9.195402e-02 0.3581498887
## <NA> 11704 7.432527e-01 NA
mathlea_10 %>% tabyl(MIG_MTHHSPCTPROF)
## MIG_MTHHSPCTPROF n percent valid_percent
## 10-14 1 6.350416e-05 0.0002954210
## 11-19 6 3.810250e-04 0.0017725258
## 15-19 5 3.175208e-04 0.0014771049
## 20-24 3 1.905125e-04 0.0008862629
## 20-29 11 6.985458e-04 0.0032496307
## 21-39 29 1.841621e-03 0.0085672083
## 25-29 10 6.350416e-04 0.0029542097
## 30-34 4 2.540166e-04 0.0011816839
## 30-39 22 1.397092e-03 0.0064992614
## 34 1 6.350416e-05 0.0002954210
## 35-39 11 6.985458e-04 0.0032496307
## 38 1 6.350416e-05 0.0002954210
## 40-44 7 4.445291e-04 0.0020679468
## 40-49 12 7.620499e-04 0.0035450517
## 40-59 25 1.587604e-03 0.0073855244
## 41 2 1.270083e-04 0.0005908419
## 45-49 4 2.540166e-04 0.0011816839
## 50-54 2 1.270083e-04 0.0005908419
## 50-59 9 5.715374e-04 0.0026587888
## 55-59 5 3.175208e-04 0.0014771049
## 58 1 6.350416e-05 0.0002954210
## 6-9 1 6.350416e-05 0.0002954210
## 60-64 5 3.175208e-04 0.0014771049
## 60-69 5 3.175208e-04 0.0014771049
## 60-79 15 9.525624e-04 0.0044313146
## 65-69 5 3.175208e-04 0.0014771049
## 70-74 2 1.270083e-04 0.0005908419
## 70-79 2 1.270083e-04 0.0005908419
## 75-79 1 6.350416e-05 0.0002954210
## 80-84 1 6.350416e-05 0.0002954210
## 80-89 1 6.350416e-05 0.0002954210
## GE50 102 6.477424e-03 0.0301329394
## GE80 6 3.810250e-04 0.0017725258
## LE10 3 1.905125e-04 0.0008862629
## LE20 13 8.255541e-04 0.0038404727
## LT50 141 8.954086e-03 0.0416543575
## n/a 1963 1.246587e-01 0.5799113737
## PS 948 6.020194e-02 0.2800590842
## <NA> 12362 7.850384e-01 NA
mathlea_10 %>% tabyl(CWD_MTHHSPCTPROF)
## CWD_MTHHSPCTPROF n percent valid_percent
## 10 2 1.270083e-04 1.725328e-04
## 10-14 131 8.319045e-03 1.130090e-02
## 11 11 6.985458e-04 9.489303e-04
## 11-19 250 1.587604e-02 2.156660e-02
## 12 3 1.905125e-04 2.587992e-04
## 13 3 1.905125e-04 2.587992e-04
## 14 2 1.270083e-04 1.725328e-04
## 15 5 3.175208e-04 4.313320e-04
## 15-19 153 9.716136e-03 1.319876e-02
## 16 5 3.175208e-04 4.313320e-04
## 17 3 1.905125e-04 2.587992e-04
## 18 2 1.270083e-04 1.725328e-04
## 19 6 3.810250e-04 5.175983e-04
## 2 1 6.350416e-05 8.626639e-05
## 20 3 1.905125e-04 2.587992e-04
## 20-24 128 8.128532e-03 1.104210e-02
## 20-29 267 1.695561e-02 2.303313e-02
## 21 8 5.080333e-04 6.901311e-04
## 21-39 624 3.962660e-02 5.383023e-02
## 22 6 3.810250e-04 5.175983e-04
## 24 2 1.270083e-04 1.725328e-04
## 25 9 5.715374e-04 7.763975e-04
## 25-29 116 7.366483e-03 1.000690e-02
## 26 4 2.540166e-04 3.450656e-04
## 27 2 1.270083e-04 1.725328e-04
## 28 4 2.540166e-04 3.450656e-04
## 29 1 6.350416e-05 8.626639e-05
## 30 4 2.540166e-04 3.450656e-04
## 30-34 111 7.048962e-03 9.575569e-03
## 30-39 243 1.543151e-02 2.096273e-02
## 32 5 3.175208e-04 4.313320e-04
## 33 3 1.905125e-04 2.587992e-04
## 34 7 4.445291e-04 6.038647e-04
## 35 1 6.350416e-05 8.626639e-05
## 35-39 81 5.143837e-03 6.987578e-03
## 36 3 1.905125e-04 2.587992e-04
## 37 3 1.905125e-04 2.587992e-04
## 38 2 1.270083e-04 1.725328e-04
## 39 1 6.350416e-05 8.626639e-05
## 40 5 3.175208e-04 4.313320e-04
## 40-44 93 5.905887e-03 8.022774e-03
## 40-49 177 1.124024e-02 1.526915e-02
## 40-59 445 2.825935e-02 3.838854e-02
## 41 7 4.445291e-04 6.038647e-04
## 42 1 6.350416e-05 8.626639e-05
## 43 2 1.270083e-04 1.725328e-04
## 44 4 2.540166e-04 3.450656e-04
## 45 3 1.905125e-04 2.587992e-04
## 45-49 74 4.699308e-03 6.383713e-03
## 46 1 6.350416e-05 8.626639e-05
## 47 2 1.270083e-04 1.725328e-04
## 48 1 6.350416e-05 8.626639e-05
## 49 4 2.540166e-04 3.450656e-04
## 50 2 1.270083e-04 1.725328e-04
## 50-54 51 3.238712e-03 4.399586e-03
## 50-59 129 8.192037e-03 1.112836e-02
## 51 2 1.270083e-04 1.725328e-04
## 52 1 6.350416e-05 8.626639e-05
## 53 2 1.270083e-04 1.725328e-04
## 54 1 6.350416e-05 8.626639e-05
## 55 2 1.270083e-04 1.725328e-04
## 55-59 47 2.984695e-03 4.054520e-03
## 57 2 1.270083e-04 1.725328e-04
## 58 1 6.350416e-05 8.626639e-05
## 59 1 6.350416e-05 8.626639e-05
## 6-9 66 4.191275e-03 5.693582e-03
## 60 1 6.350416e-05 8.626639e-05
## 60-64 37 2.349654e-03 3.191856e-03
## 60-69 113 7.175970e-03 9.748102e-03
## 60-79 255 1.619356e-02 2.199793e-02
## 61 1 6.350416e-05 8.626639e-05
## 62 2 1.270083e-04 1.725328e-04
## 63 1 6.350416e-05 8.626639e-05
## 65 2 1.270083e-04 1.725328e-04
## 65-69 39 2.476662e-03 3.364389e-03
## 66 1 6.350416e-05 8.626639e-05
## 67 1 6.350416e-05 8.626639e-05
## 69 2 1.270083e-04 1.725328e-04
## 7 1 6.350416e-05 8.626639e-05
## 70-74 25 1.587604e-03 2.156660e-03
## 70-79 71 4.508795e-03 6.124914e-03
## 73 1 6.350416e-05 8.626639e-05
## 75 1 6.350416e-05 8.626639e-05
## 75-79 27 1.714612e-03 2.329193e-03
## 76 1 6.350416e-05 8.626639e-05
## 77 2 1.270083e-04 1.725328e-04
## 78 2 1.270083e-04 1.725328e-04
## 80 1 6.350416e-05 8.626639e-05
## 80-84 20 1.270083e-03 1.725328e-03
## 80-89 47 2.984695e-03 4.054520e-03
## 81 2 1.270083e-04 1.725328e-04
## 82 1 6.350416e-05 8.626639e-05
## 84 1 6.350416e-05 8.626639e-05
## 85-89 15 9.525624e-04 1.293996e-03
## 86 1 6.350416e-05 8.626639e-05
## 87 1 6.350416e-05 8.626639e-05
## 9 1 6.350416e-05 8.626639e-05
## 90-94 11 6.985458e-04 9.489303e-04
## GE50 1028 6.528228e-02 8.868185e-02
## GE80 96 6.096399e-03 8.281573e-03
## GE90 27 1.714612e-03 2.329193e-03
## GE95 1 6.350416e-05 8.626639e-05
## LE10 206 1.308186e-02 1.777088e-02
## LE20 750 4.762812e-02 6.469979e-02
## LE5 27 1.714612e-03 2.329193e-03
## LT50 2342 1.487267e-01 2.020359e-01
## n/a 165 1.047819e-02 1.423395e-02
## PS 2922 1.855592e-01 2.520704e-01
## <NA> 4155 2.638598e-01 NA
Research Question:
What drives current expenditure on education? How is funding allocated by state and how do the funding allocations correlate with eh student performance (graduation rates and/or test scores)? Does cross-state variation in expenditure explain the cross-state variation in education outcomes? More specifically, I might look at the following:
Datasets:
colnames(fiscal2010)
## [1] "A07" "A08" "A09"
## [4] "A11" "A13" "A15"
## [7] "A20" "A40" "AGCHRT"
## [10] "B10" "B11" "B12"
## [13] "B13" "C01" "C04"
## [16] "C05" "C06" "C07"
## [19] "C08" "C09" "C10"
## [22] "C11" "C12" "C13"
## [25] "C14" "C15" "C16"
## [28] "C17" "C19" "C20"
## [31] "C24" "C25" "C35"
## [34] "C36" "C38" "C39"
## [37] "CBSA" "CCDNF" "CENFILE"
## [40] "CENSUSID" "CONUM" "CSA"
## [43] "D11" "D23" "E07"
## [46] "E08" "E09" "E11"
## [49] "E13" "E17" "F12"
## [52] "FIPST" "FL_19H" "FL_21F"
## [55] "FL_31F" "FL_41F" "FL_61V"
## [58] "FL_66V" "FL_A07" "FL_A08"
## [61] "FL_A09" "FL_A11" "FL_A13"
## [64] "FL_A15" "FL_A20" "FL_A40"
## [67] "FL_B10" "FL_B11" "FL_B12"
## [70] "FL_B13" "FL_C01" "FL_C04"
## [73] "FL_C05" "FL_C06" "FL_C07"
## [76] "FL_C08" "FL_C09" "FL_C10"
## [79] "FL_C11" "FL_C12" "FL_C13"
## [82] "FL_C14" "FL_C15" "FL_C16"
## [85] "FL_C17" "FL_C19" "FL_C20"
## [88] "FL_C24" "FL_C25" "FL_C35"
## [91] "FL_C36" "FL_C38" "FL_C39"
## [94] "FL_D11" "FL_D23" "FL_E07"
## [97] "FL_E08" "FL_E09" "FL_E11"
## [100] "FL_E13" "FL_E17" "FL_F12"
## [103] "FL_G15" "FL_HE1" "FL_HE2"
## [106] "FL_HR1" "FL_I86" "FL_K09"
## [109] "FL_K10" "FL_K11" "FL_L12"
## [112] "FL_M12" "FL_MEMBERSCH" "FL_Q11"
## [115] "FL_T02" "FL_T06" "FL_T09"
## [118] "FL_T15" "FL_T40" "FL_T99"
## [121] "FL_U11" "FL_U22" "FL_U30"
## [124] "FL_U50" "FL_U97" "FL_V10"
## [127] "FL_V11" "FL_V12" "FL_V13"
## [130] "FL_V14" "FL_V15" "FL_V16"
## [133] "FL_V17" "FL_V18" "FL_V21"
## [136] "FL_V22" "FL_V23" "FL_V24"
## [139] "FL_V29" "FL_V30" "FL_V32"
## [142] "FL_V33" "FL_V37" "FL_V38"
## [145] "FL_V40" "FL_V45" "FL_V60"
## [148] "FL_V65" "FL_V70" "FL_V75"
## [151] "FL_V80" "FL_V85" "FL_V90"
## [154] "FL_V91" "FL_V92" "FL_V93"
## [157] "FL_W01" "FL_W31" "FL_W61"
## [160] "FL_Z32" "FL_Z33" "FL_Z34"
## [163] "FL_Z35" "FL_Z36" "FL_Z37"
## [166] "FL_Z38" "FILEURL" "G15"
## [169] "GSHI" "GSLO" "HE1"
## [172] "HE2" "HR1" "I86"
## [175] "K09" "K10" "K11"
## [178] "L12" "LEAID" "M12"
## [181] "MEMBERSCH" "NAME" "Q11"
## [184] "SCHLEV" "STABBR" "STNAME"
## [187] "T02" "T06" "T09"
## [190] "T15" "T40" "T99"
## [193] "TCAPOUT" "TCURELSC" "TCURINST"
## [196] "TCUROTH" "TCURSSVC" "TFEDREV"
## [199] "TLOCREV" "TNONELSE" "TOTALEXP"
## [202] "TOTALREV" "TSTREV" "U11"
## [205] "U22" "U30" "U50"
## [208] "U97" "V10" "V11"
## [211] "V12" "V13" "V14"
## [214] "V15" "V16" "V17"
## [217] "V18" "V21" "V22"
## [220] "V23" "V24" "V29"
## [223] "V30" "V32" "V33"
## [226] "V37" "V38" "V40"
## [229] "V45" "V60" "V65"
## [232] "V70" "V75" "V80"
## [235] "V85" "V90" "V91"
## [238] "V92" "V93" "W01"
## [241] "W31" "W61" "WEIGHT"
## [244] "Z32" "Z33" "Z34"
## [247] "Z35" "Z36" "Z37"
## [250] "Z38" "_19H" "_21F"
## [253] "_31F" "_41F" "_61V"
## [256] "_66V" "_YEAR" "YEAR"
## [259] "PIPELINE" "DL_INGESTION_DATETIME"
skim(fiscal2010)
| Name | fiscal2010 |
| Number of rows | 18247 |
| Number of columns | 260 |
| _______________________ | |
| Column type frequency: | |
| character | 129 |
| numeric | 130 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| AGCHRT | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| CBSA | 0 | 1 | 1 | 5 | 0 | 939 | 0 |
| CENSUSID | 0 | 1 | 1 | 14 | 0 | 14842 | 0 |
| CONUM | 0 | 1 | 5 | 5 | 0 | 3129 | 0 |
| CSA | 0 | 1 | 1 | 3 | 0 | 125 | 0 |
| FIPST | 0 | 1 | 2 | 2 | 0 | 51 | 0 |
| FL_19H | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_21F | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_31F | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_41F | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_61V | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_66V | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_A07 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_A08 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_A09 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_A11 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_A13 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_A15 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_A20 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_A40 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_B10 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_B11 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_B12 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_B13 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C01 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C04 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C05 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C06 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C07 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C08 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C09 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C10 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C11 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C12 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C13 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C14 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C15 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C16 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C17 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C19 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C20 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C24 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C25 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C35 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_C36 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C38 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_C39 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_D11 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_D23 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_E07 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_E08 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_E09 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_E11 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_E13 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_E17 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_F12 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_G15 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_HE1 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_HE2 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_HR1 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_I86 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_K09 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_K10 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_K11 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_L12 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_M12 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_MEMBERSCH | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_Q11 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_T02 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_T06 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_T09 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_T15 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_T40 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_T99 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_U11 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_U22 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_U30 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_U50 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_U97 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V10 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V11 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V12 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V13 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V14 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V15 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V16 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V17 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V18 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V21 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V22 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V23 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V24 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V29 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V30 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V32 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V33 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V37 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V38 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V40 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V45 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V60 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V65 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V70 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V75 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V80 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V85 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V90 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_V91 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_V92 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_V93 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_W01 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_W31 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_W61 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_Z32 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_Z33 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_Z34 | 0 | 1 | 1 | 1 | 0 | 5 | 0 |
| FL_Z35 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_Z36 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FL_Z37 | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| FL_Z38 | 0 | 1 | 1 | 1 | 0 | 3 | 0 |
| FILEURL | 0 | 1 | 48 | 48 | 0 | 1 | 0 |
| GSHI | 0 | 1 | 1 | 2 | 0 | 16 | 0 |
| GSLO | 0 | 1 | 1 | 2 | 0 | 16 | 0 |
| LEAID | 0 | 1 | 7 | 7 | 0 | 18247 | 0 |
| NAME | 0 | 1 | 3 | 60 | 0 | 17717 | 0 |
| SCHLEV | 0 | 1 | 1 | 2 | 0 | 7 | 0 |
| STABBR | 0 | 1 | 2 | 2 | 0 | 51 | 0 |
| STNAME | 0 | 1 | 4 | 20 | 0 | 51 | 0 |
| PIPELINE | 0 | 1 | 29 | 29 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| A07 | 0 | 1 | 62321.43 | 402543.33 | -2 | 0 | 0 | 9000.0 | 20229000 | ▇▁▁▁▁ |
| A08 | 0 | 1 | 5939.67 | 128277.93 | -2 | 0 | 0 | 0.0 | 15564000 | ▇▁▁▁▁ |
| A09 | 0 | 1 | 364123.59 | 1144107.53 | -2 | 3000 | 79000 | 319000.0 | 47205000 | ▇▁▁▁▁ |
| A11 | 0 | 1 | 10957.26 | 85439.61 | -2 | 0 | 0 | 0.0 | 3510000 | ▇▁▁▁▁ |
| A13 | 0 | 1 | 211909.26 | 1823605.27 | -2 | 0 | 8000 | 97000.0 | 171949000 | ▇▁▁▁▁ |
| A15 | 0 | 1 | 8501.85 | 191879.04 | -2 | 0 | 0 | 0.0 | 14939000 | ▇▁▁▁▁ |
| A20 | 0 | 1 | 91805.46 | 808818.77 | -2 | 0 | 0 | 3000.0 | 49072000 | ▇▁▁▁▁ |
| A40 | 0 | 1 | 41828.48 | 293969.27 | -2 | 0 | 0 | 9000.0 | 19348000 | ▇▁▁▁▁ |
| B10 | 0 | 1 | 68834.01 | 917277.92 | -2 | 0 | 0 | 0.0 | 59398000 | ▇▁▁▁▁ |
| B11 | 0 | 1 | 19931.51 | 259028.50 | -2 | 0 | 0 | 0.0 | 28169000 | ▇▁▁▁▁ |
| B12 | 0 | 1 | 5454.66 | 56806.67 | -2 | 0 | 0 | 0.0 | 3111000 | ▇▁▁▁▁ |
| B13 | 0 | 1 | 162690.86 | 2006666.21 | -2 | 0 | 0 | 24000.0 | 213069000 | ▇▁▁▁▁ |
| C01 | 0 | 1 | 9904463.16 | 56726682.92 | -2 | 481000 | 2407000 | 7717500.0 | 6016802000 | ▇▁▁▁▁ |
| C04 | 0 | 1 | 177204.10 | 1791292.67 | -2 | 0 | 0 | 0.0 | 139157000 | ▇▁▁▁▁ |
| C05 | 0 | 1 | 918167.93 | 12489751.14 | -2 | 0 | 0 | 331000.0 | 1445472000 | ▇▁▁▁▁ |
| C06 | 0 | 1 | 255034.54 | 3212965.61 | -2 | 0 | 0 | 47000.0 | 262095000 | ▇▁▁▁▁ |
| C07 | 0 | 1 | 33335.24 | 848165.31 | -2 | 0 | 0 | 0.0 | 52887000 | ▇▁▁▁▁ |
| C08 | 0 | 1 | 23623.02 | 667675.15 | -2 | 0 | 0 | 0.0 | 68096000 | ▇▁▁▁▁ |
| C09 | 0 | 1 | 43071.09 | 351589.50 | -2 | 0 | 0 | 0.0 | 16140000 | ▇▁▁▁▁ |
| C10 | 0 | 1 | 28732.95 | 385107.93 | -2 | 0 | 2000 | 12000.0 | 42871000 | ▇▁▁▁▁ |
| C11 | 0 | 1 | 340673.71 | 3307919.59 | -2 | 0 | 0 | 0.0 | 289060000 | ▇▁▁▁▁ |
| C12 | 0 | 1 | 219562.73 | 1235888.02 | -2 | 0 | 0 | 68000.0 | 76286000 | ▇▁▁▁▁ |
| C13 | 0 | 1 | 1467624.72 | 15568842.66 | -2 | 1000 | 76000 | 506000.0 | 1684314000 | ▇▁▁▁▁ |
| C14 | 0 | 1 | 928407.42 | 8605443.22 | -2 | 23000 | 146000 | 508000.0 | 777993000 | ▇▁▁▁▁ |
| C15 | 0 | 1 | 783204.43 | 4229045.40 | -2 | 0 | 69000 | 473000.0 | 257678000 | ▇▁▁▁▁ |
| C16 | 0 | 1 | 91089.01 | 662844.07 | -2 | 0 | 0 | 43000.0 | 58822000 | ▇▁▁▁▁ |
| C17 | 0 | 1 | 13732.90 | 128064.08 | -2 | 0 | 0 | 4500.0 | 10177000 | ▇▁▁▁▁ |
| C19 | 0 | 1 | 34663.02 | 223558.75 | -2 | 0 | 0 | 2000.0 | 15750000 | ▇▁▁▁▁ |
| C20 | 0 | 1 | 1216737.06 | 6249021.27 | -2 | 1000 | 178000 | 753000.0 | 394540000 | ▇▁▁▁▁ |
| C24 | 0 | 1 | 198884.54 | 5846320.83 | -2 | 0 | 0 | 0.0 | 729737000 | ▇▁▁▁▁ |
| C25 | 0 | 1 | 658136.53 | 4121046.74 | -2 | 11000 | 118000 | 399500.0 | 321606000 | ▇▁▁▁▁ |
| C35 | 0 | 1 | 111686.04 | 1947391.23 | -2 | 0 | 0 | 0.0 | 208110000 | ▇▁▁▁▁ |
| C36 | 0 | 1 | 141945.32 | 4773984.42 | -2 | 0 | 0 | 0.0 | 637078000 | ▇▁▁▁▁ |
| C38 | 0 | 1 | 631631.02 | 3267240.10 | -2 | 0 | 0 | 161000.0 | 144110000 | ▇▁▁▁▁ |
| C39 | 0 | 1 | 31920.66 | 320989.51 | -2 | 0 | 0 | 0.0 | 19306000 | ▇▁▁▁▁ |
| CCDNF | 0 | 1 | 1.00 | 0.04 | 0 | 1 | 1 | 1.0 | 1 | ▁▁▁▁▇ |
| CENFILE | 0 | 1 | 0.81 | 0.39 | 0 | 1 | 1 | 1.0 | 1 | ▂▁▁▁▇ |
| D11 | 0 | 1 | 603561.64 | 3194163.22 | -2 | 0 | 0 | 208000.0 | 201357000 | ▇▁▁▁▁ |
| D23 | 0 | 1 | 442007.85 | 3606971.99 | -2 | 0 | 0 | 46000.0 | 340982000 | ▇▁▁▁▁ |
| E07 | 0 | 1 | 1366785.35 | 6893020.79 | -2 | 34000 | 227000 | 952000.0 | 479925000 | ▇▁▁▁▁ |
| E08 | 0 | 1 | 550224.54 | 1818703.71 | -2 | 101000 | 283000 | 593000.0 | 144467000 | ▇▁▁▁▁ |
| E09 | 0 | 1 | 1559184.15 | 7539644.20 | -2 | 102000 | 381000 | 1206000.0 | 528651000 | ▇▁▁▁▁ |
| E11 | 0 | 1 | 1071170.56 | 5492398.66 | -2 | 50000 | 261000 | 830000.0 | 448671000 | ▇▁▁▁▁ |
| E13 | 0 | 1 | 17327081.34 | 127893156.30 | -2 | 1171000 | 4198000 | 13455500.0 | 14936045000 | ▇▁▁▁▁ |
| E17 | 0 | 1 | 1578756.96 | 6042710.01 | -2 | 46000 | 286000 | 1203000.0 | 333309000 | ▇▁▁▁▁ |
| F12 | 0 | 1 | 2583185.58 | 27822230.17 | -2 | 0 | 37000 | 675000.0 | 3044559000 | ▇▁▁▁▁ |
| G15 | 0 | 1 | 181778.44 | 1733083.39 | -2 | 0 | 0 | 0.0 | 106072000 | ▇▁▁▁▁ |
| HE1 | 0 | 1 | 1388857.91 | 10400685.61 | -2 | 55000 | 289000 | 968000.0 | 1050214000 | ▇▁▁▁▁ |
| HE2 | 0 | 1 | 64137.90 | 490090.60 | -2 | 0 | 0 | 24000.0 | 36903000 | ▇▁▁▁▁ |
| HR1 | 0 | 1 | 218646.58 | 2227187.59 | -2 | 0 | 26000 | 115000.0 | 232282000 | ▇▁▁▁▁ |
| I86 | 0 | 1 | 971108.03 | 6437069.74 | -2 | 0 | 64000 | 529000.0 | 439446000 | ▇▁▁▁▁ |
| K09 | 0 | 1 | 137397.72 | 665773.23 | -2 | 0 | 18000 | 95000.0 | 48388000 | ▇▁▁▁▁ |
| K10 | 0 | 1 | 353665.65 | 1589523.52 | -2 | 2000 | 65000 | 242000.0 | 61852000 | ▇▁▁▁▁ |
| K11 | 0 | 1 | 23581.48 | 246991.88 | -2 | 0 | 0 | 0.0 | 12401000 | ▇▁▁▁▁ |
| L12 | 0 | 1 | 80185.74 | 1349349.83 | -2 | 0 | 0 | 0.0 | 111938000 | ▇▁▁▁▁ |
| M12 | 0 | 1 | 11888.16 | 391698.39 | -2 | 0 | 0 | 0.0 | 49058000 | ▇▁▁▁▁ |
| MEMBERSCH | 0 | 1 | 2690.61 | 12815.67 | -9 | 167 | 641 | 2083.5 | 1014020 | ▇▁▁▁▁ |
| Q11 | 0 | 1 | 702918.96 | 7421719.40 | -2 | 0 | 33000 | 283000.0 | 634364000 | ▇▁▁▁▁ |
| T02 | 0 | 1 | 2639423.57 | 73594947.69 | -2 | -2 | -2 | -2.0 | 9073697000 | ▇▁▁▁▁ |
| T06 | 0 | 1 | 9271963.74 | 39717090.27 | -2 | -2 | 1187000 | 5982000.0 | 1818529000 | ▇▁▁▁▁ |
| T09 | 0 | 1 | 203461.80 | 3394275.08 | -2 | -2 | 0 | 0.0 | 161332000 | ▇▁▁▁▁ |
| T15 | 0 | 1 | 19918.86 | 255405.26 | -2 | -2 | 0 | 0.0 | 20748000 | ▇▁▁▁▁ |
| T40 | 0 | 1 | 99695.20 | 1287236.16 | -2 | -2 | 0 | 0.0 | 110682000 | ▇▁▁▁▁ |
| T99 | 0 | 1 | 74279.41 | 909702.46 | -2 | -2 | 0 | 0.0 | 58982000 | ▇▁▁▁▁ |
| TCAPOUT | 0 | 1 | 3279609.48 | 29521711.94 | -2 | 35000 | 285000 | 1333000.0 | 3151607000 | ▇▁▁▁▁ |
| TCURELSC | 0 | 1 | 28436776.91 | 179053170.29 | -2 | 2109000 | 7146000 | 22648500.0 | 19453219000 | ▇▁▁▁▁ |
| TCURINST | 0 | 1 | 17327081.34 | 127893156.30 | -2 | 1171000 | 4198000 | 13455500.0 | 14936045000 | ▇▁▁▁▁ |
| TCUROTH | 0 | 1 | 1140166.50 | 5600778.27 | -2 | 55500 | 283000 | 874000.0 | 448671000 | ▇▁▁▁▁ |
| TCURSSVC | 0 | 1 | 9969528.76 | 48510265.33 | -2 | 776000 | 2588000 | 8035000.0 | 4068503000 | ▇▁▁▁▁ |
| TFEDREV | 0 | 1 | 4124828.42 | 25252763.77 | -2 | 261000 | 899000 | 2680000.0 | 2047926000 | ▇▁▁▁▁ |
| TLOCREV | 0 | 1 | 15170253.20 | 97409268.54 | -2 | 499000 | 3031000 | 10780000.0 | 10600597000 | ▇▁▁▁▁ |
| TNONELSE | 0 | 1 | 363810.50 | 3018302.79 | -2 | 0 | 3000 | 109000.0 | 175673000 | ▇▁▁▁▁ |
| TOTALEXP | 0 | 1 | 34140053.39 | 224183538.28 | -2 | 2408000 | 8445000 | 26676000.0 | 24597709000 | ▇▁▁▁▁ |
| TOTALREV | 0 | 1 | 33481814.83 | 199406458.00 | -2 | 2434500 | 8493000 | 26767000.0 | 21023695000 | ▇▁▁▁▁ |
| TSTREV | 0 | 1 | 14186732.90 | 85066587.68 | -2 | 945000 | 3617000 | 11322500.0 | 8375172000 | ▇▁▁▁▁ |
| U11 | 0 | 1 | 20131.32 | 332556.14 | -2 | 0 | 0 | 0.0 | 28720000 | ▇▁▁▁▁ |
| U22 | 0 | 1 | 108350.53 | 735676.31 | -2 | 1000 | 12000 | 56000.0 | 73023000 | ▇▁▁▁▁ |
| U30 | 0 | 1 | 19000.78 | 222943.67 | -2 | 0 | 0 | 0.0 | 19854000 | ▇▁▁▁▁ |
| U50 | 0 | 1 | 52941.97 | 586124.91 | -2 | 0 | 0 | 13000.0 | 48929000 | ▇▁▁▁▁ |
| U97 | 0 | 1 | 619238.19 | 11118869.62 | -2 | 6000 | 52000 | 244000.0 | 1421630000 | ▇▁▁▁▁ |
| V10 | 0 | 1 | 3998687.90 | 38204603.80 | -2 | 186000 | 870000 | 3139000.0 | 4756409000 | ▇▁▁▁▁ |
| V11 | 0 | 1 | 1053954.52 | 4087112.12 | -2 | 15000 | 176000 | 789500.0 | 243582000 | ▇▁▁▁▁ |
| V12 | 0 | 1 | 336373.22 | 1395789.52 | -2 | 2000 | 51000 | 247000.0 | 83188000 | ▇▁▁▁▁ |
| V13 | 0 | 1 | 806004.12 | 4234643.26 | -2 | 10000 | 118000 | 543000.0 | 321611000 | ▇▁▁▁▁ |
| V14 | 0 | 1 | 263026.59 | 1390817.64 | -2 | 2000 | 37000 | 186000.0 | 108630000 | ▇▁▁▁▁ |
| V15 | 0 | 1 | 239975.18 | 825385.53 | -2 | 34000 | 142000 | 269000.0 | 82474000 | ▇▁▁▁▁ |
| V16 | 0 | 1 | 92164.15 | 323598.91 | -2 | 8000 | 42000 | 99000.0 | 25761000 | ▇▁▁▁▁ |
| V17 | 0 | 1 | 1107942.03 | 5242875.32 | -2 | 63500 | 257000 | 835000.0 | 341063000 | ▇▁▁▁▁ |
| V18 | 0 | 1 | 370250.30 | 2069478.16 | -2 | 15000 | 79000 | 289000.0 | 160978000 | ▇▁▁▁▁ |
| V21 | 0 | 1 | 978273.54 | 6346261.99 | -2 | 31000 | 184000 | 681500.0 | 661475000 | ▇▁▁▁▁ |
| V22 | 0 | 1 | 388822.12 | 3028190.56 | -2 | 8000 | 68000 | 276000.0 | 327418000 | ▇▁▁▁▁ |
| V23 | 0 | 1 | 416869.36 | 1827291.51 | -2 | 0 | 46000 | 272500.0 | 68954000 | ▇▁▁▁▁ |
| V24 | 0 | 1 | 167106.71 | 771339.05 | -2 | 0 | 12000 | 97000.0 | 34626000 | ▇▁▁▁▁ |
| V29 | 0 | 1 | 357825.95 | 2204270.87 | -2 | 0 | 69000 | 259000.0 | 221534000 | ▇▁▁▁▁ |
| V30 | 0 | 1 | 131409.06 | 743125.50 | -2 | 0 | 20000 | 89000.0 | 69482000 | ▇▁▁▁▁ |
| V32 | 0 | 1 | 4498.39 | 71129.59 | -2 | 0 | 0 | 0.0 | 4338000 | ▇▁▁▁▁ |
| V33 | 0 | 1 | 2696.60 | 12832.19 | -9 | 165 | 643 | 2092.0 | 1014020 | ▇▁▁▁▁ |
| V37 | 0 | 1 | 446569.69 | 2413022.59 | -2 | 0 | 87000 | 315000.0 | 237834000 | ▇▁▁▁▁ |
| V38 | 0 | 1 | 187349.27 | 1238275.08 | -2 | 0 | 29000 | 122000.0 | 117724000 | ▇▁▁▁▁ |
| V40 | 0 | 1 | 2712894.19 | 16354709.32 | -2 | 187000 | 690000 | 2114500.0 | 1661029000 | ▇▁▁▁▁ |
| V45 | 0 | 1 | 1219745.94 | 8586494.05 | -2 | 35000 | 269000 | 978500.0 | 1024981000 | ▇▁▁▁▁ |
| V60 | 0 | 1 | 62758.66 | 644426.67 | -2 | 0 | 0 | 0.0 | 31334000 | ▇▁▁▁▁ |
| V65 | 0 | 1 | 6236.98 | 120315.98 | -2 | 0 | 0 | 0.0 | 9815000 | ▇▁▁▁▁ |
| V70 | 0 | 1 | 201312.99 | 1906604.78 | -2 | 0 | 0 | 43000.0 | 118338000 | ▇▁▁▁▁ |
| V75 | 0 | 1 | 110767.97 | 1647700.62 | -2 | 0 | 0 | 0.0 | 148516000 | ▇▁▁▁▁ |
| V80 | 0 | 1 | 51729.23 | 615328.22 | -2 | 0 | 0 | 0.0 | 40513000 | ▇▁▁▁▁ |
| V85 | 0 | 1 | 248.65 | 33609.35 | -2 | 0 | 0 | 0.0 | 4540000 | ▇▁▁▁▁ |
| V90 | 0 | 1 | 981687.90 | 6017663.61 | -2 | 43000 | 202000 | 676000.0 | 591836000 | ▇▁▁▁▁ |
| V91 | 0 | 1 | 180739.64 | 5702851.48 | -2 | 0 | 0 | 0.0 | 738402000 | ▇▁▁▁▁ |
| V92 | 0 | 1 | 113014.75 | 3320447.49 | -2 | 0 | 0 | 0.0 | 383908000 | ▇▁▁▁▁ |
| V93 | 0 | 1 | 132874.73 | 1178546.64 | -2 | 0 | 8000 | 70000.0 | 115987000 | ▇▁▁▁▁ |
| W01 | 0 | 1 | 864911.54 | 8526711.15 | -2 | 0 | 0 | 182000.0 | 701763000 | ▇▁▁▁▁ |
| W31 | 0 | 1 | 2592562.11 | 31381207.74 | -2 | 0 | 0 | 132000.0 | 3650875000 | ▇▁▁▁▁ |
| W61 | 0 | 1 | 5791300.75 | 25187070.39 | -2 | 79000 | 1284000 | 4379500.0 | 2178242000 | ▇▁▁▁▁ |
| WEIGHT | 0 | 1 | 1.00 | 0.00 | 1 | 1 | 1 | 1.0 | 1 | ▁▁▇▁▁ |
| Z32 | 0 | 1 | 17275789.18 | 99544925.43 | -2 | 1078000 | 4110000 | 13449000.0 | 10254583000 | ▇▁▁▁▁ |
| Z33 | 0 | 1 | 11689210.40 | 76409484.15 | -2 | 719000 | 2755000 | 9035000.0 | 8613741000 | ▇▁▁▁▁ |
| Z34 | 0 | 1 | 6003717.44 | 46330908.53 | -2 | 284000 | 1321000 | 4773000.0 | 5409968000 | ▇▁▁▁▁ |
| Z35 | 0 | 1 | 5979280.17 | 39123700.39 | -2 | 0 | 497000 | 4110500.0 | 4011575000 | ▇▁▁▁▁ |
| Z36 | 0 | 1 | 1360212.70 | 14071446.23 | -2 | 0 | 41000 | 776000.0 | 1713079000 | ▇▁▁▁▁ |
| Z37 | 0 | 1 | 234278.74 | 3988673.40 | -2 | 0 | 0 | 89000.0 | 524807000 | ▇▁▁▁▁ |
| Z38 | 0 | 1 | 296060.84 | 1767268.94 | -2 | 0 | 0 | 111000.0 | 78518000 | ▇▁▁▁▁ |
| _19H | 0 | 1 | 21207890.62 | 143384108.88 | -2 | 0 | 1330000 | 12291500.0 | 11615909000 | ▇▁▁▁▁ |
| _21F | 0 | 1 | 2646400.13 | 39506333.52 | -2 | 0 | 0 | 0.0 | 4342818000 | ▇▁▁▁▁ |
| _31F | 0 | 1 | 1937930.41 | 16398282.10 | -2 | 0 | 138000 | 925000.0 | 1210275000 | ▇▁▁▁▁ |
| _41F | 0 | 1 | 21921404.30 | 163411365.20 | -2 | 0 | 1347000 | 12785500.0 | 12642529000 | ▇▁▁▁▁ |
| _61V | 0 | 1 | 502922.79 | 6044542.90 | -2 | 0 | 0 | 0.0 | 625105000 | ▇▁▁▁▁ |
| _66V | 0 | 1 | 437682.37 | 3431237.57 | -2 | 0 | 0 | 0.0 | 230000000 | ▇▁▁▁▁ |
| _YEAR | 0 | 1 | 10.00 | 0.00 | 10 | 10 | 10 | 10.0 | 10 | ▁▁▇▁▁ |
| YEAR | 0 | 1 | 2010.00 | 0.00 | 2010 | 2010 | 2010 | 2010.0 | 2010 | ▁▁▇▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DL_INGESTION_DATETIME | 0 | 1 | 2021-09-02 13:02:24 | 2021-09-02 13:02:24 | 2021-09-02 13:02:24 | 1 |
# Total spendings by state
total <-fiscal2010 %>%
group_by(STNAME) %>%
summarise(TOTALEXP = sum(TOTALEXP))
total %>%
ggplot(aes(TOTALEXP, STNAME)) +
geom_col()
#in descending order
total %>%
ggplot(aes(TOTALEXP, fct_reorder(STNAME, TOTALEXP))) +
geom_col()
#spending on instruction by state
instruction <- fiscal2010 %>%
group_by(STNAME) %>%
summarise(E13 = sum(E13))
instruction %>%
ggplot(aes(E13, STNAME)) +
geom_col()
instruction %>%
ggplot(aes(E13, fct_reorder(STNAME, E13))) +
geom_col()
#New York spends more on instruction, while CA spends the most total
#spending on textbooks by state
textbooks <- fiscal2010 %>%
group_by(STNAME) %>%
summarise(V93 = sum(V93))
textbooks %>%
ggplot(aes(V93, fct_reorder(STNAME, V93))) +
geom_col()
#spending on Special Ed teachers
SpEd <- fiscal2010 %>%
group_by(STNAME) %>%
summarise(Z36 = sum(Z36))
SpEd %>%
ggplot(aes(Z36, fct_reorder(STNAME, Z36))) +
geom_col()
#next step - figure out how to show those spendings per state as a proportion of the total spending if that state
Research Question:
What is the relationship between the local revenue of local education agencies and students’ literacy outcomes on statewide assessments in 2010? Additional areas of exploration included (a) average total local revenue and local revenue property taxes by state, (b) the relationship between outcomes and total local revenue vs. local revenue from property taxes, (c) the relationship between both types of funding and outcomes by state, and (d) relationship between both types of revenue and outcomes of student subgroups (e.g., race/ethnicity, disability status, language proficiency, SES).
Datasets:
Variables:
EDFacts_rla_achievement_lea_2010_2019
NCES_CCD_fiscal_district_2010:
Cleaning and Wrangling
# Selected columns of interest. Filtered so that rows with suppressed values (e.g., -9, -2) for key variables of interest weren't included.
viz3_fiscal2010 <- fiscal2010 %>%
select(LEAID, NAME, STABBR, CENSUSID, V33,
TOTALREV, TFEDREV, TSTREV, TLOCREV, T06) %>%
filter(V33 > 0, TLOCREV > 0, T06 > 0) %>%
rename_with(tolower) %>%
rename(totalstu = v33, tlocrevtaxes = t06) %>%
mutate(locrev_stu = tlocrev / totalstu,
locrevtaxes_stu = tlocrevtaxes / totalstu)
# Narrowed the RLA 2010 file down to variables of interest. Selected percent proficient variables across all grades (00) for race ethnicity, disability, English Language Learner status, and economically disadvantaged subgroups. Transformed variable names to be lowercase, transformed state names to be title case, and replaced the suppressed values (e.g., PS, n/a, etc.) with NA.
viz3_rlalea00 <- rlalea_10 %>%
select(YEAR,
STNAM,
FIPST,
LEAID,
ALL_RLA00PCTPROF,
MAM_RLA00PCTPROF,
MAS_RLA00PCTPROF,
MBL_RLA00PCTPROF,
MHI_RLA00PCTPROF,
MTR_RLA00PCTPROF,
MWH_RLA00PCTPROF,
CWD_RLA00PCTPROF,
ECD_RLA00PCTPROF,
LEP_RLA00PCTPROF) %>%
rename_with(tolower) %>%
mutate(stnam = str_to_title(stnam),
(across(all_rla00pctprof:lep_rla00pctprof,
~replace(., . %in% c("PS",
"n/a",
"LT50",
"LE5",
"LE20",
"LE10",
"GE99",
"GE95",
"GE90",
"GE80",
"GE50"), NA))))
# Next step was cleaning the percentage columns to change percentage ranges to average percentages. I used the method Daniel used on the course data webpage and applied it to all subgroups.
# All = across all students
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(all_rla00pctprof, c("all_lower", "all_upper"), sep = "-") %>%
mutate(
all_upper = ifelse(is.na(all_upper), all_lower, all_upper),
all_lower = as.numeric(all_lower),
all_upper = as.numeric(all_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_all = mean(c(all_lower, all_upper))) %>%
ungroup()
# mam = American Indian/Alaska Native
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mam_rla00pctprof, c("mam_lower", "mam_upper"), sep = "-") %>%
mutate(
mam_upper = ifelse(is.na(mam_upper), mam_lower, mam_upper),
mam_lower = as.numeric(mam_lower),
mam_upper = as.numeric(mam_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mam = mean(c(mam_lower, mam_upper))) %>%
ungroup()
# mas = Asian/Pacific Islander
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mas_rla00pctprof, c("mas_lower", "mas_upper"), sep = "-") %>%
mutate(
mas_upper = ifelse(is.na(mas_upper), mas_lower, mas_upper),
mas_lower = as.numeric(mas_lower),
mas_upper = as.numeric(mas_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mas = mean(c(mas_lower, mas_upper))) %>%
ungroup()
# mbl = Black
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mbl_rla00pctprof, c("mbl_lower", "mbl_upper"), sep = "-") %>%
mutate(
mbl_upper = ifelse(is.na(mbl_upper), mbl_lower, mbl_upper),
mbl_lower = as.numeric(mbl_lower),
mbl_upper = as.numeric(mbl_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mbl = mean(c(mbl_lower, mbl_upper))) %>%
ungroup()
# mhi = Hispanic/Latino
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mhi_rla00pctprof, c("mhi_lower", "mhi_upper"), sep = "-") %>%
mutate(
mhi_upper = ifelse(is.na(mhi_upper), mhi_lower, mhi_upper),
mhi_lower = as.numeric(mhi_lower),
mhi_upper = as.numeric(mhi_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mhi = mean(c(mhi_lower, mhi_upper))) %>%
ungroup()
# mtr = Multiracial
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mtr_rla00pctprof, c("mtr_lower", "mtr_upper"), sep = "-") %>%
mutate(
mtr_upper = ifelse(is.na(mtr_upper), mtr_lower, mtr_upper),
mtr_lower = as.numeric(mtr_lower),
mtr_upper = as.numeric(mtr_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mtr = mean(c(mtr_lower, mtr_upper))) %>%
ungroup()
# mwh = White
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(mwh_rla00pctprof, c("mwh_lower", "mwh_upper"), sep = "-") %>%
mutate(
mwh_upper = ifelse(is.na(mwh_upper), mwh_lower, mwh_upper),
mwh_lower = as.numeric(mwh_lower),
mwh_upper = as.numeric(mwh_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_mwh = mean(c(mwh_lower, mwh_upper))) %>%
ungroup()
# cwd = children with disabilities
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(cwd_rla00pctprof, c("cwd_lower", "cwd_upper"), sep = "-") %>%
mutate(
cwd_upper = ifelse(is.na(cwd_upper), cwd_lower, cwd_upper),
cwd_lower = as.numeric(cwd_lower),
cwd_upper = as.numeric(cwd_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_cwd = mean(c(cwd_lower, cwd_upper))) %>%
ungroup()
# ecd = economically disadvantaged
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(ecd_rla00pctprof, c("ecd_lower", "ecd_upper"), sep = "-") %>%
mutate(
ecd_upper = ifelse(is.na(ecd_upper), ecd_lower, ecd_upper),
ecd_lower = as.numeric(ecd_lower),
ecd_upper = as.numeric(ecd_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_ecd = mean(c(ecd_lower, ecd_upper))) %>%
ungroup()
# lep = limited English proficiency (English Language Learner)
viz3_rlalea00 <- viz3_rlalea00 %>%
tidyr::separate(lep_rla00pctprof, c("lep_lower", "lep_upper"), sep = "-") %>%
mutate(
lep_upper = ifelse(is.na(lep_upper), lep_lower, lep_upper),
lep_lower = as.numeric(lep_lower),
lep_upper = as.numeric(lep_upper)
) %>%
rowwise() %>%
mutate(meanpctprof_lep = mean(c(lep_lower, lep_upper))) %>%
ungroup()
# Get ride of the "_lower" and "_upper" percentage columns since they won't be needed
viz3_rlalea00 <- viz3_rlalea00 %>%
select(year,
stnam,
fipst,
leaid,
contains("meanpctprof"))
# Pivoted the dataset longer to have a column for subgroup and a column for mean percentage proficient.
viz3_rlalea00_long <- viz3_rlalea00 %>%
pivot_longer(
cols = contains("meanpctprof"),
names_to = "subgroup",
values_to = "meanpctprof",
names_prefix = "meanpctprof_")
# Joined the long file with the cleaned/narrowed fiscal data file. Used an inner join because I'm only interested in LEAs that have both student proficiency and fiscal data.
viz3_rla00long_fiscal_2010 <- inner_join(viz3_rlalea00_long, viz3_fiscal2010, by = "leaid")
Potential Visualizations
These are the data visualizations I am thinking I will choose from for our final product. I’m not going to include all of these. My primary next step is to narrow them down. Please note that they are still a bit rough and need refinement (some more than others). Aside from determining which visualizations to include, I plan to finalize color, try out annotations and/or highlighting, and explore alternate options for faceting by state. If I include the plots with fitted lines, I plan to update the colors and replace the legend with annotations.
First off, bar graphs. The first two summarize LEA local revenue and LEA local revenue from property taxes averaged by state. The third graph summarizes the average percentage of students scoring at/above proficient by state.
# Bar graph: Average LEA total local revenue ($ per student) by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
group_by(stnam) %>%
summarize(mean_locrev_stu = mean(locrev_stu)) %>%
ggplot(aes(x = mean_locrev_stu, y = fct_reorder(stnam, mean_locrev_stu))) +
geom_col(color = "white", alpha = .6) +
scale_x_continuous(expand = c(0, 0),
breaks = c(0, 2500, 5000, 7500, 10000, 12500),
labels = scales::dollar) +
labs(title = "Average Local Revenue of LEAs",
y = "State",
x = "Dollar per student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
# Bar graph: Average LEA local revenue from property taxes ($ per student) by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
group_by(stnam) %>%
summarize(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>%
ggplot(aes(x = mean_locrevtaxes_stu, y = fct_reorder(stnam, mean_locrevtaxes_stu))) +
geom_col(color = "white", alpha = .6) +
scale_x_continuous(expand = c(0, 0),
breaks = c(0, 2500, 5000, 7500, 10000),
labels = scales::dollar) +
labs(title = "Average Local Revenue of LEAs from Property Taxes",
y = "State",
x = "Dollar per student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
# Bar graph of average RLA proficiency for each state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
group_by(stnam) %>%
summarize(mean_pctprof = mean(meanpctprof, na.rm = T)) %>%
ggplot(aes(x = mean_pctprof, y = fct_reorder(stnam, mean_pctprof))) +
geom_col(color = "white", alpha = .6) +
scale_x_continuous(expand = c(0, 0),
breaks = c(0, 20, 40, 60, 80),
labels = c("0%", "20%", "40%", "60%", "80%")) +
labs(title = "Average Proficiency in Reading/Language Arts",
subtitle = "Students in Grades 3 through HS",
y = "State",
x = "Average Percentage",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Density ridges of local revenue from property taxes for LEAs by state. One thing I plan to do, if I include this plot, that I didn’t get to is sorting the states by mean revenue.
# Density ridges of local revenue from property taxes across LEAs by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrevtaxes_stu, y = stnam)) +
geom_density_ridges(fill = "cornflower blue", color = "white", alpha = .8) +
theme_minimal() +
labs(title = "Local Revenue from Property Taxes in LEAS of Each State",
y = "State",
x = "Dollar per student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot") +
scale_x_continuous(expand = c(0, 0)) +
coord_cartesian(xlim = c(0, 25000))
Scatterplots showing the relationship between types of revenue and average percentage of students scoring at/above proficient on statewide assessments of reading/language arts. Note that I used a log transformation of the x-axis to spread the points out. Without the transformation, the bulk of the points were clustered near the bottom of the range. I think that I could use highlighting and annotations for the lowest and highest points.
# Scatterplots: All students, LEA total local revenue ($ per stu) and LEA local revenue from property taxes by approx. pct proficient
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Scatterplots faceted by state.
# Scatterplot: All students - relationship between local revenue ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
# Scatterplot: All students - relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Scatterplots showing the relationship between revenue and outcomes, faceted by subgroup. These are still pretty rough (e.g., labels need work).
# Scatterplot: Relationship between local revenue ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~subgroup) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>%
ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
facet_wrap(~subgroup) +
geom_point(color = "gray30", fill = "gray30", alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Playing around with fitting lines. These are still rough. I need to work on the legend and want to try replacing it with annotations. Also could consider changing the color to highlight a specific group or pick a different color palette.
viz3_rla00long_fiscal_2010 %>%
filter(subgroup != "all") %>%
ggplot() +
geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Fitted lines faceted by state (also very rough).
viz3_rla00long_fiscal_2010 %>%
filter(subgroup != "all") %>%
ggplot() +
geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
facet_wrap(~stnam) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
y = "Approximate Average Percent Proficient",
x = "Dollar per Student",
caption = "Source: National Center for Education Statistics, 2010") +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Created maps displaying (a) average LEA local revenue in each state and (b) average LEA local revenue from property taxes in each state. I still need to fill in the missing states and want to try out different color palettes
viz3_us <- usa_sf()
viz3_map <- left_join(viz3_rla00long_fiscal_2010,
viz3_us,
by = c("stnam" = "name"))
viz3_map %>%
filter(subgroup == "all") %>%
group_by(stnam) %>%
mutate(mean_locrev_stu = mean(locrev_stu)) %>%
ggplot(aes(geometry = geometry, fill = mean_locrev_stu)) +
geom_sf(color = "white", size = 0) +
scale_fill_viridis(option = "magma",
name = "Dollar per student",
breaks = c(0, 2500, 5000, 7500, 10000, 12500),
labels = c("$0",
"$2,500",
"$5,000",
"$7,500",
"$10,000",
"$12,5000")) +
theme_void() +
labs(title = "Average LEA Total Local Revenue",
caption = "Source: National Center for Education Statistics, 2010") +
theme(plot.title.position = "plot")
viz3_map %>%
filter(subgroup == "all") %>%
group_by(stnam) %>%
mutate(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>%
ggplot(aes(geometry = geometry, fill = mean_locrevtaxes_stu)) +
geom_sf(color = "white", size = 0) +
scale_fill_viridis(option = "magma",
name = "Dollar per student",
breaks = c(0, 2500, 5000, 7500, 10000),
labels = c("$0",
"$2,500",
"$5,000",
"$7,500",
"$10,000")) +
theme_void() +
labs(title = "Average LEA Local Revenue from Property Taxes",
caption = "Source: National Center for Education Statistics, 2010") +
theme(plot.title.position = "plot")
Other Data Visualizations
These are some of the preliminary visualizations I did that I don’t think I’m moving forward with.
# Scatterplot: Total local LEA revenue from property taxes ($ per stu) x approx. pct proficient, color = subgroup
viz3_rla00long_fiscal_2010 %>%
filter(subgroup != "all") %>%
ggplot(aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
theme_minimal() +
labs(title = "Revenue from property tax x meanpctprof, color by subgroup") +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank())
Fitted lines for specific states:
# fitted lines in a few specific states
viz3_rla00long_fiscal_2010 %>%
filter(stnam == "Montana") %>%
ggplot() +
geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
scale_x_log10(labels = scales::dollar) +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank()) +
labs(title = "Montana")
viz3_rla00long_fiscal_2010 %>%
filter(stnam == "South Dakota") %>%
ggplot() +
geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
scale_x_log10(labels = scales::dollar) +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank()) +
labs(title = "South Dakota")
viz3_rla00long_fiscal_2010 %>%
filter(stnam == "New Jersey") %>%
ggplot() +
geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
scale_x_log10(labels = scales::dollar) +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank()) +
labs(title = "New Jersey")
# Fitting a line over data points for local revenue x meanpctprof by state
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "all") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
geom_smooth(method = "lm") +
facet_wrap(~stnam) +
geom_point(alpha = .1) +
scale_x_log10(labels = scales::dollar)
# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state, color by subgroup
viz3_rla00long_fiscal_2010 %>%
filter(subgroup != "all") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof, color = subgroup)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar)
Scatterplots for each student subgroup between local revenue from property tax and % proficient, faceted by state. The subgroup is indicated in the title.
# Scatterplots: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state for each subgroup
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mam") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "American Indian/Alaska Native")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mas") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Asian/Pacific Islander")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mhi") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Hispanic/Latino")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mbl") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Black")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mwh") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "White")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "mtr") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Multiracial")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "cwd") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Students with Disabilities")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "ecd") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Economically Disadvantaged")
viz3_rla00long_fiscal_2010 %>%
filter(subgroup == "lep") %>%
ggplot(aes(x = locrev_stu, y = meanpctprof)) +
facet_wrap(~stnam) +
geom_point(alpha = .4) +
scale_x_log10(labels = scales::dollar) +
labs(title = "Limited English Proficiency")